defaut timeout

* update readme for gemma 2

.github/workflows/release.yaml

@@ -437,6 +437,7 @@ jobs:
     env:
       OLLAMA_SKIP_IMAGE_BUILD: '1'
       PUSH: '1'
+      GH_TOKEN: ${{ github.token }}
     steps:
       - uses: actions/checkout@v4
       - name: Set Version
@@ -460,15 +461,20 @@ jobs:
           ls -lh dist/
           (cd dist; sha256sum * > sha256sum.txt)
           cat dist/sha256sum.txt
-      - uses: ncipollo/release-action@v1
-        with:
-          name: ${{ env.RELEASE_VERSION }}
-          allowUpdates: true
-          artifacts: 'dist/*'
-          draft: true
-          prerelease: true
-          omitBodyDuringUpdate: true
-          generateReleaseNotes: true
-          omitDraftDuringUpdate: true
-          omitPrereleaseDuringUpdate: true
-          replacesArtifacts: true
+      - name: Create or update Release
+        run: |
+          echo "Looking for existing release for ${{ env.RELEASE_VERSION }}"
+          OLD_TAG=$(gh release ls --json name,tagName | jq -r ".[] | select(.name == \"${{ env.RELEASE_VERSION }}\") | .tagName")
+          if [ -n "$OLD_TAG" ]; then
+            echo "Updating release ${{ env.RELEASE_VERSION }} to point to new tag ${GITHUB_REF_NAME}"
+            gh release edit ${OLD_TAG} --tag ${GITHUB_REF_NAME}
+          else
+            echo "Creating new release ${{ env.RELEASE_VERSION }} pointing to tag ${GITHUB_REF_NAME}"
+            gh release create ${GITHUB_REF_NAME} \
+              --title ${{ env.RELEASE_VERSION }} \
+              --draft \
+              --generate-notes \
+              --prerelease
+          fi
+          echo "Uploading artifacts for tag ${GITHUB_REF_NAME}"
+          gh release upload ${GITHUB_REF_NAME} dist/* --clobber

.github/workflows/test.yaml

@@ -124,7 +124,7 @@ jobs:
     strategy:
       matrix:
         rocm-version:
-          - '6.0.2'
+          - '6.1.1'
     runs-on: linux
     container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
     steps:

Dockerfile

@@ -2,7 +2,7 @@ ARG GOLANG_VERSION=1.22.1
 ARG CMAKE_VERSION=3.22.1
 # this CUDA_VERSION corresponds with the one specified in docs/gpu.md
 ARG CUDA_VERSION=11.3.1
-ARG ROCM_VERSION=6.0.2
+ARG ROCM_VERSION=6.1.1
 
 # Copy the minimal context we need to run the generate scripts
 FROM scratch AS llm-code

README.md

@@ -53,8 +53,8 @@ Here are some example models that can be downloaded:
 | Llama 3            | 70B        | 40GB  | `ollama run llama3:70b`        |
 | Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`              |
 | Phi 3 Medium       | 14B        | 7.9GB | `ollama run phi3:medium`       |
-| Gemma              | 2B         | 1.4GB | `ollama run gemma:2b`          |
-| Gemma              | 7B         | 4.8GB | `ollama run gemma:7b`          |
+| Gemma 2            | 9B         | 5.5GB | `ollama run gemma2`            |
+| Gemma 2            | 27B        | 16GB  | `ollama run gemma2:27b`        |
 | Mistral            | 7B         | 4.1GB | `ollama run mistral`           |
 | Moondream 2        | 1.4B       | 829MB | `ollama run moondream`         |
 | Neural Chat        | 7B         | 4.1GB | `ollama run neural-chat`       |
@@ -182,6 +182,12 @@ $ ollama run llama3 "Summarize this file: $(cat README.md)"
  Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
 ```
 
+### Show model information
+
+```
+ollama show llama3
+```
+
 ### List models on your computer
 
 ```

api/types.go

@@ -159,18 +159,49 @@ type Options struct {
 
 // Runner options which must be set when the model is loaded into memory
 type Runner struct {
-	UseNUMA   bool `json:"numa,omitempty"`
-	NumCtx    int  `json:"num_ctx,omitempty"`
-	NumBatch  int  `json:"num_batch,omitempty"`
-	NumGPU    int  `json:"num_gpu,omitempty"`
-	MainGPU   int  `json:"main_gpu,omitempty"`
-	LowVRAM   bool `json:"low_vram,omitempty"`
-	F16KV     bool `json:"f16_kv,omitempty"`
-	LogitsAll bool `json:"logits_all,omitempty"`
-	VocabOnly bool `json:"vocab_only,omitempty"`
-	UseMMap   bool `json:"use_mmap,omitempty"`
-	UseMLock  bool `json:"use_mlock,omitempty"`
-	NumThread int  `json:"num_thread,omitempty"`
+	UseNUMA   bool     `json:"numa,omitempty"`
+	NumCtx    int      `json:"num_ctx,omitempty"`
+	NumBatch  int      `json:"num_batch,omitempty"`
+	NumGPU    int      `json:"num_gpu,omitempty"`
+	MainGPU   int      `json:"main_gpu,omitempty"`
+	LowVRAM   bool     `json:"low_vram,omitempty"`
+	F16KV     bool     `json:"f16_kv,omitempty"`
+	LogitsAll bool     `json:"logits_all,omitempty"`
+	VocabOnly bool     `json:"vocab_only,omitempty"`
+	UseMMap   TriState `json:"use_mmap,omitempty"`
+	UseMLock  bool     `json:"use_mlock,omitempty"`
+	NumThread int      `json:"num_thread,omitempty"`
+}
+
+type TriState int
+
+const (
+	TriStateUndefined TriState = -1
+	TriStateFalse     TriState = 0
+	TriStateTrue      TriState = 1
+)
+
+func (b *TriState) UnmarshalJSON(data []byte) error {
+	var v bool
+	if err := json.Unmarshal(data, &v); err != nil {
+		return err
+	}
+	if v {
+		*b = TriStateTrue
+	}
+	*b = TriStateFalse
+	return nil
+}
+
+func (b *TriState) MarshalJSON() ([]byte, error) {
+	if *b == TriStateUndefined {
+		return nil, nil
+	}
+	var v bool
+	if *b == TriStateTrue {
+		v = true
+	}
+	return json.Marshal(v)
 }
 
 // EmbeddingRequest is the request passed to [Client.Embeddings].
@@ -222,6 +253,7 @@ type ShowRequest struct {
 	Model    string `json:"model"`
 	System   string `json:"system"`
 	Template string `json:"template"`
+	Verbose  bool   `json:"verbose"`
 
 	Options map[string]interface{} `json:"options"`
 
@@ -231,13 +263,16 @@ type ShowRequest struct {
 
 // ShowResponse is the response returned from [Client.Show].
 type ShowResponse struct {
-	License    string       `json:"license,omitempty"`
-	Modelfile  string       `json:"modelfile,omitempty"`
-	Parameters string       `json:"parameters,omitempty"`
-	Template   string       `json:"template,omitempty"`
-	System     string       `json:"system,omitempty"`
-	Details    ModelDetails `json:"details,omitempty"`
-	Messages   []Message    `json:"messages,omitempty"`
+	License       string         `json:"license,omitempty"`
+	Modelfile     string         `json:"modelfile,omitempty"`
+	Parameters    string         `json:"parameters,omitempty"`
+	Template      string         `json:"template,omitempty"`
+	System        string         `json:"system,omitempty"`
+	Details       ModelDetails   `json:"details,omitempty"`
+	Messages      []Message      `json:"messages,omitempty"`
+	ModelInfo     map[string]any `json:"model_info,omitempty"`
+	ProjectorInfo map[string]any `json:"projector_info,omitempty"`
+	ModifiedAt    time.Time      `json:"modified_at,omitempty"`
 }
 
 // CopyRequest is the request passed to [Client.Copy].
@@ -402,6 +437,19 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 				continue
 			}
 
+			if reflect.PointerTo(field.Type()) == reflect.TypeOf((*TriState)(nil)) {
+				val, ok := val.(bool)
+				if !ok {
+					return fmt.Errorf("option %q must be of type boolean", key)
+				}
+				if val {
+					field.SetInt(int64(TriStateTrue))
+				} else {
+					field.SetInt(int64(TriStateFalse))
+				}
+				continue
+			}
+
 			switch field.Kind() {
 			case reflect.Int:
 				switch t := val.(type) {
@@ -490,7 +538,7 @@ func DefaultOptions() Options {
 			LowVRAM:   false,
 			F16KV:     true,
 			UseMLock:  false,
-			UseMMap:   true,
+			UseMMap:   TriStateUndefined,
 			UseNUMA:   false,
 		},
 	}
@@ -560,6 +608,19 @@ func FormatParams(params map[string][]string) (map[string]interface{}, error) {
 		} else {
 			field := valueOpts.FieldByName(opt.Name)
 			if field.IsValid() && field.CanSet() {
+				if reflect.PointerTo(field.Type()) == reflect.TypeOf((*TriState)(nil)) {
+					boolVal, err := strconv.ParseBool(vals[0])
+					if err != nil {
+						return nil, fmt.Errorf("invalid bool value %s", vals)
+					}
+					if boolVal {
+						out[key] = TriStateTrue
+					} else {
+						out[key] = TriStateFalse
+					}
+					continue
+				}
+
 				switch field.Kind() {
 				case reflect.Float32:
 					floatVal, err := strconv.ParseFloat(vals[0], 32)

api/types_test.go

@@ -2,6 +2,7 @@ package api
 
 import (
 	"encoding/json"
+	"fmt"
 	"math"
 	"testing"
 	"time"
@@ -105,3 +106,101 @@ func TestDurationMarshalUnmarshal(t *testing.T) {
 		})
 	}
 }
+
+func TestUseMmapParsingFromJSON(t *testing.T) {
+	tests := []struct {
+		name string
+		req  string
+		exp  TriState
+	}{
+		{
+			name: "Undefined",
+			req:  `{ }`,
+			exp:  TriStateUndefined,
+		},
+		{
+			name: "True",
+			req:  `{ "use_mmap": true }`,
+			exp:  TriStateTrue,
+		},
+		{
+			name: "False",
+			req:  `{ "use_mmap": false }`,
+			exp:  TriStateFalse,
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			var oMap map[string]interface{}
+			err := json.Unmarshal([]byte(test.req), &oMap)
+			require.NoError(t, err)
+			opts := DefaultOptions()
+			err = opts.FromMap(oMap)
+			require.NoError(t, err)
+			assert.Equal(t, test.exp, opts.UseMMap)
+		})
+	}
+}
+
+func TestUseMmapFormatParams(t *testing.T) {
+	tests := []struct {
+		name string
+		req  map[string][]string
+		exp  TriState
+		err  error
+	}{
+		{
+			name: "True",
+			req: map[string][]string{
+				"use_mmap": []string{"true"},
+			},
+			exp: TriStateTrue,
+			err: nil,
+		},
+		{
+			name: "False",
+			req: map[string][]string{
+				"use_mmap": []string{"false"},
+			},
+			exp: TriStateFalse,
+			err: nil,
+		},
+		{
+			name: "Numeric True",
+			req: map[string][]string{
+				"use_mmap": []string{"1"},
+			},
+			exp: TriStateTrue,
+			err: nil,
+		},
+		{
+			name: "Numeric False",
+			req: map[string][]string{
+				"use_mmap": []string{"0"},
+			},
+			exp: TriStateFalse,
+			err: nil,
+		},
+		{
+			name: "invalid string",
+			req: map[string][]string{
+				"use_mmap": []string{"foo"},
+			},
+			exp: TriStateUndefined,
+			err: fmt.Errorf("invalid bool value [foo]"),
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			resp, err := FormatParams(test.req)
+			require.Equal(t, err, test.err)
+			respVal, ok := resp["use_mmap"]
+			if test.exp != TriStateUndefined {
+				assert.True(t, ok, "resp: %v", resp)
+				assert.Equal(t, test.exp, respVal)
+			}
+		})
+	}
+}

app/lifecycle/logging.go

@@ -5,6 +5,8 @@ import (
 	"log/slog"
 	"os"
 	"path/filepath"
+	"strconv"
+	"strings"
 
 	"github.com/ollama/ollama/envconfig"
 )
@@ -24,6 +26,7 @@ func InitLogging() {
 		logFile = os.Stderr
 		// TODO - write one-line to the app.log file saying we're running in console mode to help avoid confusion
 	} else {
+		rotateLogs(AppLogFile)
 		logFile, err = os.OpenFile(AppLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
 		if err != nil {
 			slog.Error(fmt.Sprintf("failed to create server log %v", err))
@@ -46,3 +49,32 @@ func InitLogging() {
 
 	slog.Info("ollama app started")
 }
+
+func rotateLogs(logFile string) {
+	if _, err := os.Stat(logFile); os.IsNotExist(err) {
+		return
+	}
+	index := strings.LastIndex(logFile, ".")
+	pre := logFile[:index]
+	post := "." + logFile[index+1:]
+	for i := LogRotationCount; i > 0; i-- {
+		older := pre + "-" + strconv.Itoa(i) + post
+		newer := pre + "-" + strconv.Itoa(i-1) + post
+		if i == 1 {
+			newer = pre + post
+		}
+		if _, err := os.Stat(newer); err == nil {
+			if _, err := os.Stat(older); err == nil {
+				err := os.Remove(older)
+				if err != nil {
+					slog.Warn("Failed to remove older log", "older", older, "error", err)
+					continue
+				}
+			}
+			err := os.Rename(newer, older)
+			if err != nil {
+				slog.Warn("Failed to rotate log", "older", older, "newer", newer, "error", err)
+			}
+		}
+	}
+}

app/lifecycle/logging_test.go

@@ -0,0 +1,44 @@
+package lifecycle
+
+import (
+	"os"
+	"path/filepath"
+	"strconv"
+	"testing"
+
+	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
+)
+
+func TestRotateLogs(t *testing.T) {
+	logDir := t.TempDir()
+	logFile := filepath.Join(logDir, "testlog.log")
+
+	// No log exists
+	rotateLogs(logFile)
+
+	require.NoError(t, os.WriteFile(logFile, []byte("1"), 0644))
+	assert.FileExists(t, logFile)
+	// First rotation
+	rotateLogs(logFile)
+	assert.FileExists(t, filepath.Join(logDir, "testlog-1.log"))
+	assert.NoFileExists(t, filepath.Join(logDir, "testlog-2.log"))
+	assert.NoFileExists(t, logFile)
+
+	// Should be a no-op without a new log
+	rotateLogs(logFile)
+	assert.FileExists(t, filepath.Join(logDir, "testlog-1.log"))
+	assert.NoFileExists(t, filepath.Join(logDir, "testlog-2.log"))
+	assert.NoFileExists(t, logFile)
+
+	for i := 2; i <= LogRotationCount+1; i++ {
+		require.NoError(t, os.WriteFile(logFile, []byte(strconv.Itoa(i)), 0644))
+		assert.FileExists(t, logFile)
+		rotateLogs(logFile)
+		assert.NoFileExists(t, logFile)
+		for j := 1; j < i; j++ {
+			assert.FileExists(t, filepath.Join(logDir, "testlog-"+strconv.Itoa(j)+".log"))
+		}
+		assert.NoFileExists(t, filepath.Join(logDir, "testlog-"+strconv.Itoa(i+1)+".log"))
+	}
+}

app/lifecycle/paths.go

@@ -16,11 +16,12 @@ var (
 	AppDir     = "/opt/Ollama"
 	AppDataDir = "/opt/Ollama"
 	// TODO - should there be a distinct log dir?
-	UpdateStageDir = "/tmp"
-	AppLogFile     = "/tmp/ollama_app.log"
-	ServerLogFile  = "/tmp/ollama.log"
-	UpgradeLogFile = "/tmp/ollama_update.log"
-	Installer      = "OllamaSetup.exe"
+	UpdateStageDir   = "/tmp"
+	AppLogFile       = "/tmp/ollama_app.log"
+	ServerLogFile    = "/tmp/ollama.log"
+	UpgradeLogFile   = "/tmp/ollama_update.log"
+	Installer        = "OllamaSetup.exe"
+	LogRotationCount = 5
 )
 
 func init() {

app/lifecycle/server.go

@@ -54,7 +54,7 @@ func start(ctx context.Context, command string) (*exec.Cmd, error) {
 		return nil, fmt.Errorf("failed to spawn server stderr pipe: %w", err)
 	}
 
-	// TODO - rotation
+	rotateLogs(ServerLogFile)
 	logFile, err := os.OpenFile(ServerLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
 	if err != nil {
 		return nil, fmt.Errorf("failed to create server log: %w", err)

app/ollama.iss

@@ -88,10 +88,15 @@ DialogFontSize=12
 [Files]
 Source: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit
 Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit
-Source: "..\dist\windows-{#ARCH}\*.dll"; DestDir: "{app}"; Flags: ignoreversion 64bit
 Source: "..\dist\windows-{#ARCH}\ollama_runners\*"; DestDir: "{app}\ollama_runners"; Flags: ignoreversion 64bit recursesubdirs
 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
 Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion
+#if DirExists("..\dist\windows-amd64\cuda")
+  Source: "..\dist\windows-amd64\cuda\*"; DestDir: "{app}\cuda\"; Flags: ignoreversion recursesubdirs
+#endif
+#if DirExists("..\dist\windows-amd64\oneapi")
+  Source: "..\dist\windows-amd64\oneapi\*"; DestDir: "{app}\oneapi\"; Flags: ignoreversion recursesubdirs
+#endif
 #if DirExists("..\dist\windows-amd64\rocm")
   Source: "..\dist\windows-amd64\rocm\*"; DestDir: "{app}\rocm\"; Flags: ignoreversion recursesubdirs
 #endif

cmd/cmd.go

@@ -162,9 +162,6 @@ func tempZipFiles(path string) (string, error) {
 	}
 	defer tempfile.Close()
 
-	zipfile := zip.NewWriter(tempfile)
-	defer zipfile.Close()
-
 	detectContentType := func(path string) (string, error) {
 		f, err := os.Open(path)
 		if err != nil {
@@ -233,6 +230,9 @@ func tempZipFiles(path string) (string, error) {
 		files = append(files, tks...)
 	}
 
+	zipfile := zip.NewWriter(tempfile)
+	defer zipfile.Close()
+
 	for _, file := range files {
 		f, err := os.Open(file)
 		if err != nil {
@@ -287,38 +287,12 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string) (string, er
 }
 
 func RunHandler(cmd *cobra.Command, args []string) error {
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
-	}
-
-	name := args[0]
-
-	// check if the model exists on the server
-	show, err := client.Show(cmd.Context(), &api.ShowRequest{Name: name})
-	var statusError api.StatusError
-	switch {
-	case errors.As(err, &statusError) && statusError.StatusCode == http.StatusNotFound:
-		if err := PullHandler(cmd, []string{name}); err != nil {
-			return err
-		}
-
-		show, err = client.Show(cmd.Context(), &api.ShowRequest{Name: name})
-		if err != nil {
-			return err
-		}
-	case err != nil:
-		return err
-	}
-
 	interactive := true
 
 	opts := runOptions{
-		Model:       args[0],
-		WordWrap:    os.Getenv("TERM") == "xterm-256color",
-		Options:     map[string]interface{}{},
-		MultiModal:  slices.Contains(show.Details.Families, "clip"),
-		ParentModel: show.Details.ParentModel,
+		Model:    args[0],
+		WordWrap: os.Getenv("TERM") == "xterm-256color",
+		Options:  map[string]interface{}{},
 	}
 
 	format, err := cmd.Flags().GetString("format")
@@ -362,11 +336,38 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 	opts.WordWrap = !nowrap
 
-	if !interactive {
-		return generate(cmd, opts)
+	// Fill out the rest of the options based on information about the
+	// model.
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
 	}
 
-	return generateInteractive(cmd, opts)
+	name := args[0]
+	info, err := func() (*api.ShowResponse, error) {
+		showReq := &api.ShowRequest{Name: name}
+		info, err := client.Show(cmd.Context(), showReq)
+		var se api.StatusError
+		if errors.As(err, &se) && se.StatusCode == http.StatusNotFound {
+			if err := PullHandler(cmd, []string{name}); err != nil {
+				return nil, err
+			}
+			return client.Show(cmd.Context(), &api.ShowRequest{Name: name})
+		}
+		return info, err
+	}()
+	if err != nil {
+		return err
+	}
+
+	opts.MultiModal = slices.Contains(info.Details.Families, "clip")
+	opts.ParentModel = info.Details.ParentModel
+	opts.Messages = append(opts.Messages, info.Messages...)
+
+	if interactive {
+		return generateInteractive(cmd, opts)
+	}
+	return generate(cmd, opts)
 }
 
 func errFromUnknownKey(unknownKeyErr error) error {
@@ -579,10 +580,6 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	if len(args) != 1 {
-		return errors.New("missing model name")
-	}
-
 	license, errLicense := cmd.Flags().GetBool("license")
 	modelfile, errModelfile := cmd.Flags().GetBool("modelfile")
 	parameters, errParams := cmd.Flags().GetBool("parameters")
@@ -625,8 +622,29 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 
 	if flagsSet > 1 {
 		return errors.New("only one of '--license', '--modelfile', '--parameters', '--system', or '--template' can be specified")
-	} else if flagsSet == 0 {
-		return errors.New("one of '--license', '--modelfile', '--parameters', '--system', or '--template' must be specified")
+	}
+
+	if flagsSet == 1 {
+		req := api.ShowRequest{Name: args[0]}
+		resp, err := client.Show(cmd.Context(), &req)
+		if err != nil {
+			return err
+		}
+
+		switch showType {
+		case "license":
+			fmt.Println(resp.License)
+		case "modelfile":
+			fmt.Println(resp.Modelfile)
+		case "parameters":
+			fmt.Println(resp.Parameters)
+		case "system":
+			fmt.Println(resp.System)
+		case "template":
+			fmt.Println(resp.Template)
+		}
+
+		return nil
 	}
 
 	req := api.ShowRequest{Name: args[0]}
@@ -635,22 +653,114 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	switch showType {
-	case "license":
-		fmt.Println(resp.License)
-	case "modelfile":
-		fmt.Println(resp.Modelfile)
-	case "parameters":
-		fmt.Println(resp.Parameters)
-	case "system":
-		fmt.Println(resp.System)
-	case "template":
-		fmt.Println(resp.Template)
+	arch := resp.ModelInfo["general.architecture"].(string)
+
+	modelData := [][]string{
+		{"arch", arch},
+		{"parameters", resp.Details.ParameterSize},
+		{"quantization", resp.Details.QuantizationLevel},
+		{"context length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))},
+		{"embedding length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64))},
 	}
 
+	mainTableData := [][]string{
+		{"Model"},
+		{renderSubTable(modelData, false)},
+	}
+
+	if resp.ProjectorInfo != nil {
+		projectorData := [][]string{
+			{"arch", "clip"},
+			{"parameters", format.HumanNumber(uint64(resp.ProjectorInfo["general.parameter_count"].(float64)))},
+			{"projector type", resp.ProjectorInfo["clip.projector_type"].(string)},
+			{"embedding length", fmt.Sprintf("%v", resp.ProjectorInfo["clip.vision.embedding_length"].(float64))},
+			{"projection dimensionality", fmt.Sprintf("%v", resp.ProjectorInfo["clip.vision.projection_dim"].(float64))},
+		}
+
+		mainTableData = append(mainTableData,
+			[]string{"Projector"},
+			[]string{renderSubTable(projectorData, false)},
+		)
+	}
+
+	if resp.Parameters != "" {
+		mainTableData = append(mainTableData, []string{"Parameters"}, []string{formatParams(resp.Parameters)})
+	}
+
+	if resp.System != "" {
+		mainTableData = append(mainTableData, []string{"System"}, []string{renderSubTable(twoLines(resp.System), true)})
+	}
+
+	if resp.License != "" {
+		mainTableData = append(mainTableData, []string{"License"}, []string{renderSubTable(twoLines(resp.License), true)})
+	}
+
+	table := tablewriter.NewWriter(os.Stdout)
+	table.SetAutoWrapText(false)
+	table.SetBorder(false)
+	table.SetAlignment(tablewriter.ALIGN_LEFT)
+
+	for _, v := range mainTableData {
+		table.Append(v)
+	}
+
+	table.Render()
+
 	return nil
 }
 
+func renderSubTable(data [][]string, file bool) string {
+	var buf bytes.Buffer
+	table := tablewriter.NewWriter(&buf)
+	table.SetAutoWrapText(!file)
+	table.SetBorder(false)
+	table.SetNoWhiteSpace(true)
+	table.SetTablePadding("\t")
+	table.SetAlignment(tablewriter.ALIGN_LEFT)
+
+	for _, v := range data {
+		table.Append(v)
+	}
+
+	table.Render()
+
+	renderedTable := buf.String()
+	lines := strings.Split(renderedTable, "\n")
+	for i, line := range lines {
+		lines[i] = "\t" + line
+	}
+
+	return strings.Join(lines, "\n")
+}
+
+func twoLines(s string) [][]string {
+	lines := strings.Split(s, "\n")
+	res := [][]string{}
+
+	count := 0
+	for _, line := range lines {
+		line = strings.TrimSpace(line)
+		if line != "" {
+			count++
+			res = append(res, []string{line})
+			if count == 2 {
+				return res
+			}
+		}
+	}
+	return res
+}
+
+func formatParams(s string) string {
+	lines := strings.Split(s, "\n")
+	table := [][]string{}
+
+	for _, line := range lines {
+		table = append(table, strings.Fields(line))
+	}
+	return renderSubTable(table, false)
+}
+
 func CopyHandler(cmd *cobra.Command, args []string) error {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {

cmd/interactive.go

@@ -31,65 +31,40 @@ const (
 )
 
 func loadModel(cmd *cobra.Command, opts *runOptions) error {
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
-	}
-
 	p := progress.NewProgress(os.Stderr)
 	defer p.StopAndClear()
 
 	spinner := progress.NewSpinner("")
 	p.Add("", spinner)
 
-	showReq := api.ShowRequest{Name: opts.Model}
-	showResp, err := client.Show(cmd.Context(), &showReq)
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
-	opts.MultiModal = slices.Contains(showResp.Details.Families, "clip")
-	opts.ParentModel = showResp.Details.ParentModel
-
-	if len(showResp.Messages) > 0 {
-		opts.Messages = append(opts.Messages, showResp.Messages...)
-	}
 
 	chatReq := &api.ChatRequest{
-		Model:    opts.Model,
-		Messages: []api.Message{},
+		Model:     opts.Model,
+		KeepAlive: opts.KeepAlive,
 	}
 
-	if opts.KeepAlive != nil {
-		chatReq.KeepAlive = opts.KeepAlive
-	}
-
-	err = client.Chat(cmd.Context(), chatReq, func(resp api.ChatResponse) error {
+	return client.Chat(cmd.Context(), chatReq, func(resp api.ChatResponse) error {
 		p.StopAndClear()
-		if len(opts.Messages) > 0 {
-			for _, msg := range opts.Messages {
-				switch msg.Role {
-				case "user":
-					fmt.Printf(">>> %s\n", msg.Content)
-				case "assistant":
-					state := &displayResponseState{}
-					displayResponse(msg.Content, opts.WordWrap, state)
-					fmt.Println()
-					fmt.Println()
-				}
+		for _, msg := range opts.Messages {
+			switch msg.Role {
+			case "user":
+				fmt.Printf(">>> %s\n", msg.Content)
+			case "assistant":
+				state := &displayResponseState{}
+				displayResponse(msg.Content, opts.WordWrap, state)
+				fmt.Println()
+				fmt.Println()
 			}
 		}
 		return nil
 	})
-	if err != nil {
-		return err
-	}
-
-	return nil
 }
 
 func generateInteractive(cmd *cobra.Command, opts runOptions) error {
-	opts.Messages = make([]api.Message, 0)
-
 	err := loadModel(cmd, &opts)
 	if err != nil {
 		return err

docs/api.md

@@ -777,11 +777,12 @@ A single JSON object will be returned.
 POST /api/show
 ```
 
-Show information about a model including details, modelfile, template, parameters, license, and system prompt.
+Show information about a model including details, modelfile, template, parameters, license, system prompt.
 
 ### Parameters
 
 - `name`: name of the model to show
+- `verbose`: (optional) if set to `true`, returns full data for verbose response fields
 
 ### Examples
 
@@ -798,14 +799,40 @@ curl http://localhost:11434/api/show -d '{
 ```json
 {
   "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /Users/matt/.ollama/models/blobs/sha256:200765e1283640ffbd013184bf496e261032fa75b99498a9613be4e94d63ad52\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: \"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"\u003c/s\u003e\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSISTANT:\"",
-  "parameters": "num_ctx                        4096\nstop                           \u003c/s\u003e\nstop                           USER:\nstop                           ASSISTANT:",
-  "template": "{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: ",
+  "parameters": "num_keep                       24\nstop                           \"<|start_header_id|>\"\nstop                           \"<|end_header_id|>\"\nstop                           \"<|eot_id|>\"",
+  "template": "{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>",
   "details": {
+    "parent_model": "",
     "format": "gguf",
     "family": "llama",
-    "families": ["llama", "clip"],
-    "parameter_size": "7B",
+    "families": [
+      "llama"
+    ],
+    "parameter_size": "8.0B",
     "quantization_level": "Q4_0"
+  },
+  "model_info": {
+    "general.architecture": "llama",
+    "general.file_type": 2,
+    "general.parameter_count": 8030261248,
+    "general.quantization_version": 2,
+    "llama.attention.head_count": 32,
+    "llama.attention.head_count_kv": 8,
+    "llama.attention.layer_norm_rms_epsilon": 0.00001,
+    "llama.block_count": 32,
+    "llama.context_length": 8192,
+    "llama.embedding_length": 4096,
+    "llama.feed_forward_length": 14336,
+    "llama.rope.dimension_count": 128,
+    "llama.rope.freq_base": 500000,
+    "llama.vocab_size": 128256,
+    "tokenizer.ggml.bos_token_id": 128000,
+    "tokenizer.ggml.eos_token_id": 128009,
+    "tokenizer.ggml.merges": [],            // populates if `verbose=true`
+    "tokenizer.ggml.model": "gpt2",
+    "tokenizer.ggml.pre": "llama-bpe",
+    "tokenizer.ggml.token_type": [],        // populates if `verbose=true`
+    "tokenizer.ggml.tokens": []             // populates if `verbose=true`
   }
 }
 ```

docs/development.md

@@ -114,15 +114,18 @@ If you have Docker available, you can build linux binaries with `./scripts/build
 
 ### Windows
 
-Note: The windows build for Ollama is still under development.
+Note: The Windows build for Ollama is still under development.
 
-Install required tools:
+First, install required tools:
 
 - MSVC toolchain - C/C++ and cmake as minimal requirements
 - Go version 1.22 or higher
 - MinGW (pick one variant) with GCC.
   - [MinGW-w64](https://www.mingw-w64.org/)
   - [MSYS2](https://www.msys2.org/)
+- The `ThreadJob` Powershell module: `Install-Module -Name ThreadJob -Scope CurrentUser`
+
+Then, build the `ollama` binary:
 
 ```powershell
 $env:CGO_ENABLED="1"

docs/gpu.md

@@ -8,7 +8,7 @@ Check your compute compatibility to see if your card is supported:
 | Compute Capability | Family              | Cards                                                                                                       |
 | ------------------ | ------------------- | ----------------------------------------------------------------------------------------------------------- |
 | 9.0                | NVIDIA              | `H100`                                                                                                      |
-| 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080` `RTX 4070 Ti` `RTX 4060 Ti`                                                           |
+| 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060`  |
 |                    | NVIDIA Professional | `L4` `L40` `RTX 6000`                                                                                       |
 | 8.6                | GeForce RTX 30xx    | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060`         |
 |                    | NVIDIA Professional | `A40` `RTX A6000` `RTX A5000` `RTX A4000` `RTX A3000` `RTX A2000` `A10` `A16` `A2`                          |

docs/import.md

@@ -47,19 +47,13 @@ success
 
 ### Supported Quantizations
 
-<details>
-<summary>Legacy Quantization</summary>
-
 - `Q4_0`
 - `Q4_1`
 - `Q5_0`
 - `Q5_1`
 - `Q8_0`
 
-</details>
-
-<details>
-<summary>K-means Quantization</summary>`
+#### K-means Quantizations
 
 - `Q3_K_S`
 - `Q3_K_M`
@@ -70,11 +64,6 @@ success
 - `Q5_K_M`
 - `Q6_K`
 
-</details>
-
-> [!NOTE]
-> Activation-aware Weight Quantization (i.e. IQ) are not currently supported for automatic quantization however you can still import the quantized model into Ollama, see [Import GGUF](#import-gguf).
-
 ## Template Detection
 
 > [!NOTE]

docs/openai.md

@@ -104,7 +104,6 @@ curl http://localhost:11434/v1/chat/completions \
 
 #### Notes
 
-- Setting `seed` will always set `temperature` to `0`
 - `finish_reason` will always be `stop`
 - `usage.prompt_tokens` will be 0 for completions where prompt evaluation is cached
 

docs/troubleshooting.md

@@ -22,7 +22,7 @@ docker logs <container-name>
 If manually running `ollama serve` in a terminal, the logs will be on that terminal.
 
 When you run Ollama on **Windows**, there are a few different locations. You can view them in the explorer window by hitting `<cmd>+R` and type in:
-- `explorer %LOCALAPPDATA%\Ollama` to view logs
+- `explorer %LOCALAPPDATA%\Ollama` to view logs.  The most recent server logs will be in `server.log` and older logs will be in `server-#.log` 
 - `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
 - `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories

docs/windows.md

@@ -39,8 +39,8 @@ server.
 Ollama on Windows stores files in a few different locations.  You can view them in
 the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
-    - *app.log* contains logs from the GUI application
-    - *server.log* contains the server logs
+    - *app.log* contains most resent logs from the GUI application
+    - *server.log* contains the most recent server logs
     - *upgrade.log* contains log output for upgrades
 - `explorer %LOCALAPPDATA%\Programs\Ollama` contains the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` contains models and configuration

envconfig/config.go

@@ -53,8 +53,23 @@ var (
 	NumParallel int
 	// Set via OLLAMA_RUNNERS_DIR in the environment
 	RunnersDir string
+	// Set via OLLAMA_SCHED_SPREAD in the environment
+	SchedSpread bool
 	// Set via OLLAMA_TMPDIR in the environment
 	TmpDir string
+	// Set via OLLAMA_INTEL_GPU in the environment
+	IntelGpu bool
+
+	// Set via CUDA_VISIBLE_DEVICES in the environment
+	CudaVisibleDevices string
+	// Set via HIP_VISIBLE_DEVICES in the environment
+	HipVisibleDevices string
+	// Set via ROCR_VISIBLE_DEVICES in the environment
+	RocrVisibleDevices string
+	// Set via GPU_DEVICE_ORDINAL in the environment
+	GpuDeviceOrdinal string
+	// Set via HSA_OVERRIDE_GFX_VERSION in the environment
+	HsaOverrideGfxVersion string
 )
 
 type EnvVar struct {
@@ -64,7 +79,7 @@ type EnvVar struct {
 }
 
 func AsMap() map[string]EnvVar {
-	return map[string]EnvVar{
+	ret := map[string]EnvVar{
 		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", Debug, "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
 		"OLLAMA_FLASH_ATTENTION":   {"OLLAMA_FLASH_ATTENTION", FlashAttention, "Enabled flash attention"},
 		"OLLAMA_HOST":              {"OLLAMA_HOST", Host, "IP Address for the ollama server (default 127.0.0.1:11434)"},
@@ -79,8 +94,18 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel, "Maximum number of parallel requests (default 1)"},
 		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", AllowOrigins, "A comma separated list of allowed origins"},
 		"OLLAMA_RUNNERS_DIR":       {"OLLAMA_RUNNERS_DIR", RunnersDir, "Location for runners"},
+		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread, "Always schedule model across all GPUs"},
 		"OLLAMA_TMPDIR":            {"OLLAMA_TMPDIR", TmpDir, "Location for temporary files"},
 	}
+	if runtime.GOOS != "darwin" {
+		ret["CUDA_VISIBLE_DEVICES"] = EnvVar{"CUDA_VISIBLE_DEVICES", CudaVisibleDevices, "Set which NVIDIA devices are visible"}
+		ret["HIP_VISIBLE_DEVICES"] = EnvVar{"HIP_VISIBLE_DEVICES", HipVisibleDevices, "Set which AMD devices are visible"}
+		ret["ROCR_VISIBLE_DEVICES"] = EnvVar{"ROCR_VISIBLE_DEVICES", RocrVisibleDevices, "Set which AMD devices are visible"}
+		ret["GPU_DEVICE_ORDINAL"] = EnvVar{"GPU_DEVICE_ORDINAL", GpuDeviceOrdinal, "Set which AMD devices are visible"}
+		ret["HSA_OVERRIDE_GFX_VERSION"] = EnvVar{"HSA_OVERRIDE_GFX_VERSION", HsaOverrideGfxVersion, "Override the gfx used for all detected AMD GPUs"}
+		ret["OLLAMA_INTEL_GPU"] = EnvVar{"OLLAMA_INTEL_GPU", IntelGpu, "Enable experimental Intel GPU detection"}
+	}
+	return ret
 }
 
 func Values() map[string]string {
@@ -191,6 +216,15 @@ func LoadConfig() {
 		NoHistory = true
 	}
 
+	if spread := clean("OLLAMA_SCHED_SPREAD"); spread != "" {
+		s, err := strconv.ParseBool(spread)
+		if err == nil {
+			SchedSpread = s
+		} else {
+			SchedSpread = true
+		}
+	}
+
 	if noprune := clean("OLLAMA_NOPRUNE"); noprune != "" {
 		NoPrune = true
 	}
@@ -244,6 +278,16 @@ func LoadConfig() {
 	if err != nil {
 		slog.Error("invalid setting", "OLLAMA_HOST", Host, "error", err, "using default port", Host.Port)
 	}
+
+	if set, err := strconv.ParseBool(clean("OLLAMA_INTEL_GPU")); err == nil {
+		IntelGpu = set
+	}
+
+	CudaVisibleDevices = clean("CUDA_VISIBLE_DEVICES")
+	HipVisibleDevices = clean("HIP_VISIBLE_DEVICES")
+	RocrVisibleDevices = clean("ROCR_VISIBLE_DEVICES")
+	GpuDeviceOrdinal = clean("GPU_DEVICE_ORDINAL")
+	HsaOverrideGfxVersion = clean("HSA_OVERRIDE_GFX_VERSION")
 }
 
 func getModelsDir() (string, error) {

gpu/amd_linux.go

@@ -13,6 +13,7 @@ import (
 	"strconv"
 	"strings"
 
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 )
 
@@ -25,7 +26,16 @@ const (
 
 	// Prefix with the node dir
 	GPUTotalMemoryFileGlob = "mem_banks/*/properties" // size_in_bytes line
-	GPUUsedMemoryFileGlob  = "mem_banks/*/used_memory"
+
+	// Direct Rendering Manager sysfs location
+	DRMDeviceDirGlob   = "/sys/class/drm/card*/device"
+	DRMTotalMemoryFile = "mem_info_vram_total"
+	DRMUsedMemoryFile  = "mem_info_vram_used"
+
+	// In hex; properties file is in decimal
+	DRMUniqueIDFile = "unique_id"
+	DRMVendorFile   = "vendor"
+	DRMDeviceFile   = "device"
 )
 
 var (
@@ -35,8 +45,8 @@ var (
 )
 
 // Gather GPU information from the amdgpu driver if any supported GPUs are detected
-func AMDGetGPUInfo() []GpuInfo {
-	resp := []GpuInfo{}
+func AMDGetGPUInfo() []RocmGPUInfo {
+	resp := []RocmGPUInfo{}
 	if !AMDDetected() {
 		return resp
 	}
@@ -50,9 +60,9 @@ func AMDGetGPUInfo() []GpuInfo {
 
 	// Determine if the user has already pre-selected which GPUs to look at, then ignore the others
 	var visibleDevices []string
-	hipVD := os.Getenv("HIP_VISIBLE_DEVICES")   // zero based index only
-	rocrVD := os.Getenv("ROCR_VISIBLE_DEVICES") // zero based index or UUID, but consumer cards seem to not support UUID
-	gpuDO := os.Getenv("GPU_DEVICE_ORDINAL")    // zero based index
+	hipVD := envconfig.HipVisibleDevices   // zero based index only
+	rocrVD := envconfig.RocrVisibleDevices // zero based index or UUID, but consumer cards seem to not support UUID
+	gpuDO := envconfig.GpuDeviceOrdinal    // zero based index
 	switch {
 	// TODO is this priorty order right?
 	case hipVD != "":
@@ -65,7 +75,7 @@ func AMDGetGPUInfo() []GpuInfo {
 		visibleDevices = strings.Split(gpuDO, ",")
 	}
 
-	gfxOverride := os.Getenv("HSA_OVERRIDE_GFX_VERSION")
+	gfxOverride := envconfig.HsaOverrideGfxVersion
 	var supported []string
 	libDir := ""
 
@@ -90,7 +100,7 @@ func AMDGetGPUInfo() []GpuInfo {
 		scanner := bufio.NewScanner(fp)
 		isCPU := false
 		var major, minor, patch uint64
-		var vendor, device uint64
+		var vendor, device, uniqueID uint64
 		for scanner.Scan() {
 			line := strings.TrimSpace(scanner.Text())
 			// Note: we could also use "cpu_cores_count X" where X is greater than zero to detect CPUs
@@ -121,30 +131,43 @@ func AMDGetGPUInfo() []GpuInfo {
 			} else if strings.HasPrefix(line, "vendor_id") {
 				ver := strings.Fields(line)
 				if len(ver) != 2 {
-					slog.Debug("malformed vendor_id", "vendor_id", line)
+					slog.Debug("malformed", "vendor_id", line)
 					continue
 				}
-				vendor, err = strconv.ParseUint(ver[1], 10, 32)
+				vendor, err = strconv.ParseUint(ver[1], 10, 64)
 				if err != nil {
-					slog.Debug("malformed vendor_id" + line)
+					slog.Debug("malformed", "vendor_id", line, "error", err)
 				}
 			} else if strings.HasPrefix(line, "device_id") {
 				ver := strings.Fields(line)
 				if len(ver) != 2 {
-					slog.Debug("malformed device_id", "device_id", line)
+					slog.Debug("malformed", "device_id", line)
 					continue
 				}
-				device, err = strconv.ParseUint(ver[1], 10, 32)
+				device, err = strconv.ParseUint(ver[1], 10, 64)
 				if err != nil {
-					slog.Debug("malformed device_id" + line)
+					slog.Debug("malformed", "device_id", line, "error", err)
+				}
+			} else if strings.HasPrefix(line, "unique_id") {
+				ver := strings.Fields(line)
+				if len(ver) != 2 {
+					slog.Debug("malformed", "unique_id", line)
+					continue
+				}
+				uniqueID, err = strconv.ParseUint(ver[1], 10, 64)
+				if err != nil {
+					slog.Debug("malformed", "unique_id", line, "error", err)
 				}
 			}
-
 			// TODO - any other properties we want to extract and record?
 			// vendor_id + device_id -> pci lookup for "Name"
 			// Other metrics that may help us understand relative performance between multiple GPUs
 		}
 
+		// Note: while ./mem_banks/*/used_memory exists, it doesn't appear to take other VRAM consumers
+		// into consideration, so we instead map the device over to the DRM driver sysfs nodes which
+		// do reliably report VRAM usage.
+
 		if isCPU {
 			cpuCount++
 			continue
@@ -156,7 +179,7 @@ func AMDGetGPUInfo() []GpuInfo {
 		// Shouldn't happen, but just in case...
 		if gpuID < 0 {
 			slog.Error("unexpected amdgpu sysfs data resulted in negative GPU ID, please set OLLAMA_DEBUG=1 and report an issue")
-			return []GpuInfo{}
+			return nil
 		}
 
 		if int(major) < RocmComputeMin {
@@ -167,65 +190,68 @@ func AMDGetGPUInfo() []GpuInfo {
 		// Look up the memory for the current node
 		totalMemory := uint64(0)
 		usedMemory := uint64(0)
-		propGlob := filepath.Join(AMDNodesSysfsDir, strconv.Itoa(nodeID), GPUTotalMemoryFileGlob)
-		propFiles, err := filepath.Glob(propGlob)
-		if err != nil {
-			slog.Warn("error looking up total GPU memory", "glob", propGlob, "error", err)
+		var usedFile string
+		mapping := []struct {
+			id       uint64
+			filename string
+		}{
+			{vendor, DRMVendorFile},
+			{device, DRMDeviceFile},
+			{uniqueID, DRMUniqueIDFile}, // Not all devices will report this
 		}
-		// 1 or more memory banks - sum the values of all of them
-		for _, propFile := range propFiles {
-			fp, err := os.Open(propFile)
-			if err != nil {
-				slog.Warn("failed to open sysfs node", "file", propFile, "erroir", err)
-				continue
-			}
-			defer fp.Close()
-			scanner := bufio.NewScanner(fp)
-			for scanner.Scan() {
-				line := strings.TrimSpace(scanner.Text())
-				if strings.HasPrefix(line, "size_in_bytes") {
-					ver := strings.Fields(line)
-					if len(ver) != 2 {
-						slog.Warn("malformed " + line)
-						continue
-					}
-					bankSizeInBytes, err := strconv.ParseUint(ver[1], 10, 64)
-					if err != nil {
-						slog.Warn("malformed int " + line)
-						continue
-					}
-					totalMemory += bankSizeInBytes
+		slog.Debug("mapping amdgpu to drm sysfs nodes", "amdgpu", match, "vendor", vendor, "device", device, "unique_id", uniqueID)
+		// Map over to DRM location to find the total/free memory
+		drmMatches, _ := filepath.Glob(DRMDeviceDirGlob)
+		for _, devDir := range drmMatches {
+			matched := true
+			for _, m := range mapping {
+				if m.id == 0 {
+					// Null ID means it didn't populate, so we can't use it to match
+					continue
+				}
+				filename := filepath.Join(devDir, m.filename)
+				buf, err := os.ReadFile(filename)
+				if err != nil {
+					slog.Debug("failed to read sysfs node", "file", filename, "error", err)
+					matched = false
+					break
+				}
+				// values here are in hex, strip off the lead 0x and parse so we can compare the numeric (decimal) values in amdgpu
+				cmp, err := strconv.ParseUint(strings.TrimPrefix(strings.TrimSpace(string(buf)), "0x"), 16, 64)
+				if err != nil {
+					slog.Debug("failed to parse sysfs node", "file", filename, "error", err)
+					matched = false
+					break
+				}
+				if cmp != m.id {
+					matched = false
+					break
 				}
 			}
-		}
-		if totalMemory == 0 {
-			slog.Warn("amdgpu reports zero total memory", "gpu", gpuID)
-			continue
-		}
-		usedGlob := filepath.Join(AMDNodesSysfsDir, strconv.Itoa(nodeID), GPUUsedMemoryFileGlob)
-		usedFiles, err := filepath.Glob(usedGlob)
-		if err != nil {
-			slog.Warn("error looking up used GPU memory", "glob", usedGlob, "error", err)
-			continue
-		}
-		for _, usedFile := range usedFiles {
-			fp, err := os.Open(usedFile)
-			if err != nil {
-				slog.Warn("failed to open sysfs node", "file", usedFile, "error", err)
+			if !matched {
 				continue
 			}
-			defer fp.Close()
-			data, err := io.ReadAll(fp)
+
+			// Found the matching DRM directory
+			slog.Debug("matched", "amdgpu", match, "drm", devDir)
+			totalFile := filepath.Join(devDir, DRMTotalMemoryFile)
+			buf, err := os.ReadFile(totalFile)
 			if err != nil {
-				slog.Warn("failed to read sysfs node", "file", usedFile, "error", err)
-				continue
+				slog.Debug("failed to read sysfs node", "file", totalFile, "error", err)
+				break
 			}
-			used, err := strconv.ParseUint(strings.TrimSpace(string(data)), 10, 64)
+			totalMemory, err = strconv.ParseUint(strings.TrimSpace(string(buf)), 10, 64)
 			if err != nil {
-				slog.Warn("malformed used memory", "data", string(data), "error", err)
-				continue
+				slog.Debug("failed to parse sysfs node", "file", totalFile, "error", err)
+				break
 			}
-			usedMemory += used
+
+			usedFile = filepath.Join(devDir, DRMUsedMemoryFile)
+			usedMemory, err = getFreeMemory(usedFile)
+			if err != nil {
+				slog.Debug("failed to update used memory", "error", err)
+			}
+			break
 		}
 
 		// iGPU detection, remove this check once we can support an iGPU variant of the rocm library
@@ -241,18 +267,21 @@ func AMDGetGPUInfo() []GpuInfo {
 
 		slog.Debug("amdgpu memory", "gpu", gpuID, "total", format.HumanBytes2(totalMemory))
 		slog.Debug("amdgpu memory", "gpu", gpuID, "available", format.HumanBytes2(totalMemory-usedMemory))
-		gpuInfo := GpuInfo{
-			Library: "rocm",
-			memInfo: memInfo{
-				TotalMemory: totalMemory,
-				FreeMemory:  (totalMemory - usedMemory),
+		gpuInfo := RocmGPUInfo{
+			GpuInfo: GpuInfo{
+				Library: "rocm",
+				memInfo: memInfo{
+					TotalMemory: totalMemory,
+					FreeMemory:  (totalMemory - usedMemory),
+				},
+				ID:            strconv.Itoa(gpuID),
+				Name:          name,
+				Compute:       fmt.Sprintf("gfx%d%x%x", major, minor, patch),
+				MinimumMemory: rocmMinimumMemory,
+				DriverMajor:   driverMajor,
+				DriverMinor:   driverMinor,
 			},
-			ID:            fmt.Sprintf("%d", gpuID),
-			Name:          name,
-			Compute:       fmt.Sprintf("gfx%d%x%x", major, minor, patch),
-			MinimumMemory: rocmMinimumMemory,
-			DriverMajor:   driverMajor,
-			DriverMinor:   driverMinor,
+			usedFilepath: usedFile,
 		}
 
 		// If the user wants to filter to a subset of devices, filter out if we aren't a match
@@ -276,7 +305,7 @@ func AMDGetGPUInfo() []GpuInfo {
 			libDir, err = AMDValidateLibDir()
 			if err != nil {
 				slog.Warn("unable to verify rocm library, will use cpu", "error", err)
-				return []GpuInfo{}
+				return nil
 			}
 		}
 		gpuInfo.DependencyPath = libDir
@@ -287,7 +316,7 @@ func AMDGetGPUInfo() []GpuInfo {
 				supported, err = GetSupportedGFX(libDir)
 				if err != nil {
 					slog.Warn("failed to lookup supported GFX types, falling back to CPU mode", "error", err)
-					return []GpuInfo{}
+					return nil
 				}
 				slog.Debug("rocm supported GPUs", "types", supported)
 			}
@@ -304,6 +333,11 @@ func AMDGetGPUInfo() []GpuInfo {
 			slog.Info("skipping rocm gfx compatibility check", "HSA_OVERRIDE_GFX_VERSION", gfxOverride)
 		}
 
+		// Check for env var workarounds
+		if name == "1002:687f" { // Vega RX 56
+			gpuInfo.EnvWorkarounds = append(gpuInfo.EnvWorkarounds, [2]string{"HSA_ENABLE_SDMA", "0"})
+		}
+
 		// The GPU has passed all the verification steps and is supported
 		resp = append(resp, gpuInfo)
 	}
@@ -378,3 +412,31 @@ func AMDDriverVersion() (driverMajor, driverMinor int, err error) {
 	}
 	return driverMajor, driverMinor, nil
 }
+
+func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
+	if len(gpus) == 0 {
+		return nil
+	}
+	for i := range gpus {
+		usedMemory, err := getFreeMemory(gpus[i].usedFilepath)
+		if err != nil {
+			return err
+		}
+		slog.Debug("updating rocm free memory", "gpu", gpus[i].ID, "name", gpus[i].Name, "before", format.HumanBytes2(gpus[i].FreeMemory), "now", format.HumanBytes2(gpus[i].TotalMemory-usedMemory))
+		gpus[i].FreeMemory = gpus[i].TotalMemory - usedMemory
+	}
+	return nil
+}
+
+func getFreeMemory(usedFile string) (uint64, error) {
+	buf, err := os.ReadFile(usedFile)
+	if err != nil {
+		return 0, fmt.Errorf("failed to read sysfs node %s %w", usedFile, err)
+	}
+	usedMemory, err := strconv.ParseUint(strings.TrimSpace(string(buf)), 10, 64)
+	if err != nil {
+		slog.Debug("failed to parse sysfs node", "file", usedFile, "error", err)
+		return 0, fmt.Errorf("failed to parse sysfs node %s %w", usedFile, err)
+	}
+	return usedMemory, nil
+}

gpu/amd_windows.go

@@ -7,8 +7,10 @@ import (
 	"os"
 	"path/filepath"
 	"slices"
+	"strconv"
 	"strings"
 
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 )
 
@@ -24,8 +26,8 @@ var (
 	RocmStandardLocations = []string{"C:\\Program Files\\AMD\\ROCm\\5.7\\bin"} // TODO glob?
 )
 
-func AMDGetGPUInfo() []GpuInfo {
-	resp := []GpuInfo{}
+func AMDGetGPUInfo() []RocmGPUInfo {
+	resp := []RocmGPUInfo{}
 	hl, err := NewHipLib()
 	if err != nil {
 		slog.Debug(err.Error())
@@ -52,7 +54,7 @@ func AMDGetGPUInfo() []GpuInfo {
 	}
 
 	var supported []string
-	gfxOverride := os.Getenv("HSA_OVERRIDE_GFX_VERSION")
+	gfxOverride := envconfig.HsaOverrideGfxVersion
 	if gfxOverride == "" {
 		supported, err = GetSupportedGFX(libDir)
 		if err != nil {
@@ -117,21 +119,24 @@ func AMDGetGPUInfo() []GpuInfo {
 		// v5.7 only reports VRAM used by this process, so it's completely wrong and unusable
 		slog.Debug("amdgpu memory", "gpu", i, "total", format.HumanBytes2(totalMemory))
 		slog.Debug("amdgpu memory", "gpu", i, "available", format.HumanBytes2(freeMemory))
-		gpuInfo := GpuInfo{
-			Library: "rocm",
-			memInfo: memInfo{
-				TotalMemory: totalMemory,
-				FreeMemory:  freeMemory,
-			},
-			ID:             fmt.Sprintf("%d", i), // TODO this is probably wrong if we specify visible devices
-			DependencyPath: libDir,
-			MinimumMemory:  rocmMinimumMemory,
-			Name:           name,
-			Compute:        gfx,
+		gpuInfo := RocmGPUInfo{
+			GpuInfo: GpuInfo{
+				Library: "rocm",
+				memInfo: memInfo{
+					TotalMemory: totalMemory,
+					FreeMemory:  freeMemory,
+				},
+				ID:             strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
+				DependencyPath: libDir,
+				MinimumMemory:  rocmMinimumMemory,
+				Name:           name,
+				Compute:        gfx,
 
-			// TODO - this information isn't accurate on windows, so don't report it until we find the right way to retrieve
-			// DriverMajor:    driverMajor,
-			// DriverMinor:    driverMinor,
+				// TODO - this information isn't accurate on windows, so don't report it until we find the right way to retrieve
+				// DriverMajor:    driverMajor,
+				// DriverMinor:    driverMinor,
+			},
+			index: i,
 		}
 
 		resp = append(resp, gpuInfo)
@@ -159,3 +164,30 @@ func AMDValidateLibDir() (string, error) {
 	slog.Warn("amdgpu detected, but no compatible rocm library found.  Please install ROCm")
 	return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
 }
+
+func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
+	if len(gpus) == 0 {
+		return nil
+	}
+	hl, err := NewHipLib()
+	if err != nil {
+		slog.Debug(err.Error())
+		return nil
+	}
+	defer hl.Release()
+
+	for i := range gpus {
+		err := hl.HipSetDevice(gpus[i].index)
+		if err != nil {
+			return err
+		}
+		freeMemory, _, err := hl.HipMemGetInfo()
+		if err != nil {
+			slog.Warn("get mem info", "id", i, "error", err)
+			continue
+		}
+		slog.Debug("updating rocm free memory", "gpu", gpus[i].ID, "name", gpus[i].Name, "before", format.HumanBytes2(gpus[i].FreeMemory), "now", format.HumanBytes2(freeMemory))
+		gpus[i].FreeMemory = freeMemory
+	}
+	return nil
+}

gpu/assets.go

@@ -77,20 +77,27 @@ func cleanupTmpDirs() {
 			continue
 		}
 		raw, err := os.ReadFile(filepath.Join(d, "ollama.pid"))
-		if err == nil {
-			pid, err := strconv.Atoi(string(raw))
-			if err == nil {
-				if proc, err := os.FindProcess(pid); err == nil && !errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
-					// Another running ollama, ignore this tmpdir
-					continue
-				}
-			}
-		} else {
-			slog.Debug("failed to open ollama.pid", "path", d, "error", err)
-		}
-		err = os.RemoveAll(d)
 		if err != nil {
-			slog.Debug("unable to cleanup stale tmpdir", "path", d, "error", err)
+			slog.Warn("failed to read ollama.pid", "path", d, "error", err)
+			// No pid, ignore this tmpdir
+			continue
+		}
+
+		pid, err := strconv.Atoi(string(raw))
+		if err != nil {
+			slog.Warn("failed to parse pid", "path", d, "error", err)
+			continue
+		}
+
+		proc, err := os.FindProcess(pid)
+		if err == nil && !errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
+			slog.Warn("found running ollama", "pid", pid, "path", d)
+			// Another running ollama, ignore this tmpdir
+			continue
+		}
+
+		if err := os.Remove(d); err != nil {
+			slog.Warn("unable to cleanup stale tmpdir", "path", d, "error", err)
 		}
 	}
 }

gpu/cpu_common.go

@@ -1,21 +1,16 @@
 package gpu
 
 import (
-	"log/slog"
-
 	"golang.org/x/sys/cpu"
 )
 
-func GetCPUVariant() string {
+func GetCPUCapability() CPUCapability {
 	if cpu.X86.HasAVX2 {
-		slog.Debug("CPU has AVX2")
-		return "avx2"
+		return CPUCapabilityAVX2
 	}
 	if cpu.X86.HasAVX {
-		slog.Debug("CPU has AVX")
-		return "avx"
+		return CPUCapabilityAVX
 	}
-	slog.Debug("CPU does not have vector extensions")
 	// else LCD
-	return ""
+	return CPUCapabilityNone
 }

gpu/gpu.go

@@ -24,19 +24,37 @@ import (
 	"github.com/ollama/ollama/format"
 )
 
-type handles struct {
+type cudaHandles struct {
 	deviceCount int
 	cudart      *C.cudart_handle_t
 	nvcuda      *C.nvcuda_handle_t
+	nvml        *C.nvml_handle_t
+}
+
+type oneapiHandles struct {
 	oneapi      *C.oneapi_handle_t
+	deviceCount int
 }
 
 const (
 	cudaMinimumMemory = 457 * format.MebiByte
 	rocmMinimumMemory = 457 * format.MebiByte
+	// TODO OneAPI minimum memory
 )
 
-var gpuMutex sync.Mutex
+var (
+	gpuMutex      sync.Mutex
+	bootstrapped  bool
+	cpuCapability CPUCapability
+	cpus          []CPUInfo
+	cudaGPUs      []CudaGPUInfo
+	nvcudaLibPath string
+	cudartLibPath string
+	oneapiLibPath string
+	nvmlLibPath   string
+	rocmGPUs      []RocmGPUInfo
+	oneapiGPUs    []OneapiGPUInfo
+)
 
 // With our current CUDA compile flags, older than 5.0 will not work properly
 var CudaComputeMin = [2]C.int{5, 0}
@@ -46,113 +64,113 @@ var RocmComputeMin = 9
 // TODO find a better way to detect iGPU instead of minimum memory
 const IGPUMemLimit = 1 * format.GibiByte // 512G is what they typically report, so anything less than 1G must be iGPU
 
-var CudartLinuxGlobs = []string{
-	"/usr/local/cuda/lib64/libcudart.so*",
-	"/usr/lib/x86_64-linux-gnu/nvidia/current/libcudart.so*",
-	"/usr/lib/x86_64-linux-gnu/libcudart.so*",
-	"/usr/lib/wsl/lib/libcudart.so*",
-	"/usr/lib/wsl/drivers/*/libcudart.so*",
-	"/opt/cuda/lib64/libcudart.so*",
-	"/usr/local/cuda*/targets/aarch64-linux/lib/libcudart.so*",
-	"/usr/lib/aarch64-linux-gnu/nvidia/current/libcudart.so*",
-	"/usr/lib/aarch64-linux-gnu/libcudart.so*",
-	"/usr/local/cuda/lib*/libcudart.so*",
-	"/usr/lib*/libcudart.so*",
-	"/usr/local/lib*/libcudart.so*",
-}
-
-var CudartWindowsGlobs = []string{
-	"c:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v*\\bin\\cudart64_*.dll",
-}
-
-var NvcudaLinuxGlobs = []string{
-	"/usr/local/cuda*/targets/*/lib/libcuda.so*",
-	"/usr/lib/*-linux-gnu/nvidia/current/libcuda.so*",
-	"/usr/lib/*-linux-gnu/libcuda.so*",
-	"/usr/lib/wsl/lib/libcuda.so*",
-	"/usr/lib/wsl/drivers/*/libcuda.so*",
-	"/opt/cuda/lib*/libcuda.so*",
-	"/usr/local/cuda/lib*/libcuda.so*",
-	"/usr/lib*/libcuda.so*",
-	"/usr/local/lib*/libcuda.so*",
-}
-
-var NvcudaWindowsGlobs = []string{
-	"c:\\windows\\system*\\nvcuda.dll",
-}
-
-var OneapiWindowsGlobs = []string{
-	"c:\\Windows\\System32\\DriverStore\\FileRepository\\*\\ze_intel_gpu64.dll",
-}
-
-var OneapiLinuxGlobs = []string{
-	"/usr/lib/x86_64-linux-gnu/libze_intel_gpu.so*",
-	"/usr/lib*/libze_intel_gpu.so*",
-}
-
 // Jetson devices have JETSON_JETPACK="x.y.z" factory set to the Jetpack version installed.
 // Included to drive logic for reducing Ollama-allocated overhead on L4T/Jetson devices.
 var CudaTegra string = os.Getenv("JETSON_JETPACK")
 
 // Note: gpuMutex must already be held
-func initGPUHandles() *handles {
+func initCudaHandles() *cudaHandles {
 
 	// TODO - if the ollama build is CPU only, don't do these checks as they're irrelevant and confusing
 
-	gpuHandles := &handles{}
-	var cudartMgmtName string
-	var cudartMgmtPatterns []string
-	var nvcudaMgmtName string
-	var nvcudaMgmtPatterns []string
-
-	tmpDir, _ := PayloadsDir()
-	switch runtime.GOOS {
-	case "windows":
-		cudartMgmtName = "cudart64_*.dll"
-		localAppData := os.Getenv("LOCALAPPDATA")
-		cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", cudartMgmtName)}
-		cudartMgmtPatterns = append(cudartMgmtPatterns, CudartWindowsGlobs...)
-		// Aligned with driver, we can't carry as payloads
-		nvcudaMgmtName = "nvcuda.dll"
-		nvcudaMgmtPatterns = NvcudaWindowsGlobs
-	case "linux":
-		cudartMgmtName = "libcudart.so*"
-		if tmpDir != "" {
-			// TODO - add "payloads" for subprocess
-			cudartMgmtPatterns = []string{filepath.Join(tmpDir, "cuda*", cudartMgmtName)}
-		}
-		cudartMgmtPatterns = append(cudartMgmtPatterns, CudartLinuxGlobs...)
-		// Aligned with driver, we can't carry as payloads
-		nvcudaMgmtName = "libcuda.so*"
-		nvcudaMgmtPatterns = NvcudaLinuxGlobs
-	default:
-		return gpuHandles
+	cHandles := &cudaHandles{}
+	// Short Circuit if we already know which library to use
+	if nvmlLibPath != "" {
+		cHandles.nvml, _ = LoadNVMLMgmt([]string{nvmlLibPath})
+		return cHandles
+	}
+	if nvcudaLibPath != "" {
+		cHandles.deviceCount, cHandles.nvcuda, _ = LoadNVCUDAMgmt([]string{nvcudaLibPath})
+		return cHandles
+	}
+	if cudartLibPath != "" {
+		cHandles.deviceCount, cHandles.cudart, _ = LoadCUDARTMgmt([]string{cudartLibPath})
+		return cHandles
 	}
 
-	slog.Debug("Detecting GPUs")
-	nvcudaLibPaths := FindGPULibs(nvcudaMgmtName, nvcudaMgmtPatterns)
+	slog.Debug("searching for GPU discovery libraries for NVIDIA")
+	var cudartMgmtPatterns []string
+
+	// Aligned with driver, we can't carry as payloads
+	nvcudaMgmtPatterns := NvcudaGlobs
+
+	if runtime.GOOS == "windows" {
+		localAppData := os.Getenv("LOCALAPPDATA")
+		cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", CudartMgmtName)}
+	}
+	tmpDir, _ := PayloadsDir()
+	if tmpDir != "" {
+		// TODO - add "payloads" for subprocess
+		cudartMgmtPatterns = []string{filepath.Join(tmpDir, "cuda*", CudartMgmtName)}
+	}
+	cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
+
+	if len(NvmlGlobs) > 0 {
+		nvmlLibPaths := FindGPULibs(NvmlMgmtName, NvmlGlobs)
+		if len(nvmlLibPaths) > 0 {
+			nvml, libPath := LoadNVMLMgmt(nvmlLibPaths)
+			if nvml != nil {
+				slog.Debug("nvidia-ml loaded", "library", libPath)
+				cHandles.nvml = nvml
+				nvmlLibPath = libPath
+			}
+		}
+	}
+
+	nvcudaLibPaths := FindGPULibs(NvcudaMgmtName, nvcudaMgmtPatterns)
 	if len(nvcudaLibPaths) > 0 {
 		deviceCount, nvcuda, libPath := LoadNVCUDAMgmt(nvcudaLibPaths)
 		if nvcuda != nil {
 			slog.Debug("detected GPUs", "count", deviceCount, "library", libPath)
-			gpuHandles.nvcuda = nvcuda
-			gpuHandles.deviceCount = deviceCount
-			return gpuHandles
+			cHandles.nvcuda = nvcuda
+			cHandles.deviceCount = deviceCount
+			nvcudaLibPath = libPath
+			return cHandles
 		}
 	}
 
-	cudartLibPaths := FindGPULibs(cudartMgmtName, cudartMgmtPatterns)
+	cudartLibPaths := FindGPULibs(CudartMgmtName, cudartMgmtPatterns)
 	if len(cudartLibPaths) > 0 {
 		deviceCount, cudart, libPath := LoadCUDARTMgmt(cudartLibPaths)
 		if cudart != nil {
 			slog.Debug("detected GPUs", "library", libPath, "count", deviceCount)
-			gpuHandles.cudart = cudart
-			gpuHandles.deviceCount = deviceCount
-			return gpuHandles
+			cHandles.cudart = cudart
+			cHandles.deviceCount = deviceCount
+			cudartLibPath = libPath
+			return cHandles
 		}
 	}
 
-	return gpuHandles
+	return cHandles
+}
+
+// Note: gpuMutex must already be held
+func initOneAPIHandles() *oneapiHandles {
+	oHandles := &oneapiHandles{}
+
+	// Short Circuit if we already know which library to use
+	if oneapiLibPath != "" {
+		oHandles.deviceCount, oHandles.oneapi, _ = LoadOneapiMgmt([]string{oneapiLibPath})
+		return oHandles
+	}
+
+	oneapiLibPaths := FindGPULibs(OneapiMgmtName, OneapiGlobs)
+	if len(oneapiLibPaths) > 0 {
+		oHandles.deviceCount, oHandles.oneapi, oneapiLibPath = LoadOneapiMgmt(oneapiLibPaths)
+	}
+
+	return oHandles
+}
+
+func GetCPUInfo() GpuInfoList {
+	gpuMutex.Lock()
+	if !bootstrapped {
+		gpuMutex.Unlock()
+		GetGPUInfo()
+	} else {
+		gpuMutex.Unlock()
+	}
+	return GpuInfoList{cpus[0].GpuInfo}
 }
 
 func GetGPUInfo() GpuInfoList {
@@ -160,112 +178,255 @@ func GetGPUInfo() GpuInfoList {
 	// GPUs so we can report warnings if we see Nvidia/AMD but fail to load the libraries
 	gpuMutex.Lock()
 	defer gpuMutex.Unlock()
-
-	gpuHandles := initGPUHandles()
+	needRefresh := true
+	var cHandles *cudaHandles
+	var oHandles *oneapiHandles
 	defer func() {
-		if gpuHandles.cudart != nil {
-			C.cudart_release(*gpuHandles.cudart)
+		if cHandles != nil {
+			if cHandles.cudart != nil {
+				C.cudart_release(*cHandles.cudart)
+			}
+			if cHandles.nvcuda != nil {
+				C.nvcuda_release(*cHandles.nvcuda)
+			}
+			if cHandles.nvml != nil {
+				C.nvml_release(*cHandles.nvml)
+			}
 		}
-		if gpuHandles.nvcuda != nil {
-			C.nvcuda_release(*gpuHandles.nvcuda)
+		if oHandles != nil {
+			if oHandles.oneapi != nil {
+				// TODO - is this needed?
+				C.oneapi_release(*oHandles.oneapi)
+			}
 		}
 	}()
 
-	// All our GPU builds on x86 have AVX enabled, so fallback to CPU if we don't detect at least AVX
-	cpuVariant := GetCPUVariant()
-	if cpuVariant == "" && runtime.GOARCH == "amd64" {
-		slog.Warn("CPU does not have AVX or AVX2, disabling GPU support.")
-	}
+	if !bootstrapped {
+		slog.Debug("Detecting GPUs")
+		needRefresh = false
+		cpuCapability = GetCPUCapability()
+		var memInfo C.mem_info_t
 
-	// On windows we bundle the nvidia library one level above the runner dir
-	depPath := ""
-	if runtime.GOOS == "windows" && envconfig.RunnersDir != "" {
-		depPath = filepath.Dir(envconfig.RunnersDir)
-	}
-
-	var memInfo C.mem_info_t
-	resp := []GpuInfo{}
-
-	// NVIDIA first
-	for i := range gpuHandles.deviceCount {
-		// TODO once we support CPU compilation variants of GPU libraries refine this...
-		if cpuVariant == "" && runtime.GOARCH == "amd64" {
-			continue
+		mem, err := GetCPUMem()
+		if err != nil {
+			slog.Warn("error looking up system memory", "error", err)
 		}
-		if gpuHandles.cudart != nil || gpuHandles.nvcuda != nil {
-			gpuInfo := GpuInfo{
-				Library: "cuda",
+		cpus = []CPUInfo{CPUInfo{
+			GpuInfo: GpuInfo{
+				memInfo: mem,
+				Library: "cpu",
+				Variant: cpuCapability,
+				ID:      "0",
+			},
+		}}
+
+		// Fallback to CPU mode if we're lacking required vector extensions on x86
+		if cpuCapability < GPURunnerCPUCapability && runtime.GOARCH == "amd64" {
+			slog.Warn("CPU does not have minimum vector extensions, GPU inference disabled", "required", GPURunnerCPUCapability, "detected", cpuCapability)
+			bootstrapped = true
+			// No need to do any GPU discovery, since we can't run on them
+			return GpuInfoList{cpus[0].GpuInfo}
+		}
+
+		// On windows we bundle the nvidia library one level above the runner dir
+		depPath := ""
+		if runtime.GOOS == "windows" && envconfig.RunnersDir != "" {
+			depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir), "cuda")
+		}
+
+		// Load ALL libraries
+		cHandles = initCudaHandles()
+
+		// NVIDIA
+		for i := range cHandles.deviceCount {
+			if cHandles.cudart != nil || cHandles.nvcuda != nil {
+				gpuInfo := CudaGPUInfo{
+					GpuInfo: GpuInfo{
+						Library: "cuda",
+					},
+					index: i,
+				}
+				var driverMajor int
+				var driverMinor int
+				if cHandles.cudart != nil {
+					C.cudart_bootstrap(*cHandles.cudart, C.int(i), &memInfo)
+				} else {
+					C.nvcuda_bootstrap(*cHandles.nvcuda, C.int(i), &memInfo)
+					driverMajor = int(cHandles.nvcuda.driver_major)
+					driverMinor = int(cHandles.nvcuda.driver_minor)
+				}
+				if memInfo.err != nil {
+					slog.Info("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
+					C.free(unsafe.Pointer(memInfo.err))
+					continue
+				}
+				if memInfo.major < CudaComputeMin[0] || (memInfo.major == CudaComputeMin[0] && memInfo.minor < CudaComputeMin[1]) {
+					slog.Info(fmt.Sprintf("[%d] CUDA GPU is too old. Compute Capability detected: %d.%d", i, memInfo.major, memInfo.minor))
+					continue
+				}
+				gpuInfo.TotalMemory = uint64(memInfo.total)
+				gpuInfo.FreeMemory = uint64(memInfo.free)
+				gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
+				gpuInfo.Compute = fmt.Sprintf("%d.%d", memInfo.major, memInfo.minor)
+				gpuInfo.MinimumMemory = cudaMinimumMemory
+				gpuInfo.DependencyPath = depPath
+				gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
+				gpuInfo.DriverMajor = driverMajor
+				gpuInfo.DriverMinor = driverMinor
+
+				// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
+				cudaGPUs = append(cudaGPUs, gpuInfo)
 			}
-			var driverMajor int
-			var driverMinor int
-			if gpuHandles.cudart != nil {
-				C.cudart_check_vram(*gpuHandles.cudart, C.int(i), &memInfo)
+		}
+
+		// Intel
+		if envconfig.IntelGpu {
+			oHandles = initOneAPIHandles()
+			// On windows we bundle the oneapi library one level above the runner dir
+			depPath = ""
+			if runtime.GOOS == "windows" && envconfig.RunnersDir != "" {
+				depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir), "oneapi")
+			}
+
+			for d := range oHandles.oneapi.num_drivers {
+				if oHandles.oneapi == nil {
+					// shouldn't happen
+					slog.Warn("nil oneapi handle with driver count", "count", int(oHandles.oneapi.num_drivers))
+					continue
+				}
+				devCount := C.oneapi_get_device_count(*oHandles.oneapi, C.int(d))
+				for i := range devCount {
+					gpuInfo := OneapiGPUInfo{
+						GpuInfo: GpuInfo{
+							Library: "oneapi",
+						},
+						driverIndex: int(d),
+						gpuIndex:    int(i),
+					}
+					// TODO - split bootstrapping from updating free memory
+					C.oneapi_check_vram(*oHandles.oneapi, C.int(d), i, &memInfo)
+					// TODO - convert this to MinimumMemory based on testing...
+					var totalFreeMem float64 = float64(memInfo.free) * 0.95 // work-around: leave some reserve vram for mkl lib used in ggml-sycl backend.
+					memInfo.free = C.uint64_t(totalFreeMem)
+					gpuInfo.TotalMemory = uint64(memInfo.total)
+					gpuInfo.FreeMemory = uint64(memInfo.free)
+					gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
+					gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
+					gpuInfo.DependencyPath = depPath
+					oneapiGPUs = append(oneapiGPUs, gpuInfo)
+				}
+			}
+		}
+
+		rocmGPUs = AMDGetGPUInfo()
+		bootstrapped = true
+	}
+
+	// For detected GPUs, load library if not loaded
+
+	// Refresh free memory usage
+	if needRefresh {
+		mem, err := GetCPUMem()
+		if err != nil {
+			slog.Warn("error looking up system memory", "error", err)
+		} else {
+			slog.Debug("updating system memory data",
+				slog.Group(
+					"before",
+					"total", format.HumanBytes2(cpus[0].TotalMemory),
+					"free", format.HumanBytes2(cpus[0].FreeMemory),
+				),
+				slog.Group(
+					"now",
+					"total", format.HumanBytes2(mem.TotalMemory),
+					"free", format.HumanBytes2(mem.FreeMemory),
+				),
+			)
+			cpus[0].FreeMemory = mem.FreeMemory
+		}
+
+		var memInfo C.mem_info_t
+		if cHandles == nil && len(cudaGPUs) > 0 {
+			cHandles = initCudaHandles()
+		}
+		for i, gpu := range cudaGPUs {
+			if cHandles.nvml != nil {
+				C.nvml_get_free(*cHandles.nvml, C.int(gpu.index), &memInfo.free, &memInfo.total, &memInfo.used)
+			} else if cHandles.cudart != nil {
+				C.cudart_bootstrap(*cHandles.cudart, C.int(gpu.index), &memInfo)
+			} else if cHandles.nvcuda != nil {
+				C.nvcuda_get_free(*cHandles.nvcuda, C.int(gpu.index), &memInfo.free, &memInfo.total)
+				memInfo.used = memInfo.total - memInfo.free
 			} else {
-				C.nvcuda_check_vram(*gpuHandles.nvcuda, C.int(i), &memInfo)
-				driverMajor = int(gpuHandles.nvcuda.driver_major)
-				driverMinor = int(gpuHandles.nvcuda.driver_minor)
+				// shouldn't happen
+				slog.Warn("no valid cuda library loaded to refresh vram usage")
+				break
 			}
 			if memInfo.err != nil {
-				slog.Info("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
+				slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
 				C.free(unsafe.Pointer(memInfo.err))
 				continue
 			}
-			if memInfo.major < CudaComputeMin[0] || (memInfo.major == CudaComputeMin[0] && memInfo.minor < CudaComputeMin[1]) {
-				slog.Info(fmt.Sprintf("[%d] CUDA GPU is too old. Compute Capability detected: %d.%d", i, memInfo.major, memInfo.minor))
+			if memInfo.free == 0 {
+				slog.Warn("error looking up nvidia GPU memory")
 				continue
 			}
-			gpuInfo.TotalMemory = uint64(memInfo.total)
-			gpuInfo.FreeMemory = uint64(memInfo.free)
-			gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
-			gpuInfo.Compute = fmt.Sprintf("%d.%d", memInfo.major, memInfo.minor)
-			gpuInfo.MinimumMemory = cudaMinimumMemory
-			gpuInfo.DependencyPath = depPath
-			gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
-			gpuInfo.DriverMajor = driverMajor
-			gpuInfo.DriverMinor = driverMinor
+			slog.Debug("updating cuda memory data",
+				"gpu", gpu.ID,
+				"name", gpu.Name,
+				slog.Group(
+					"before",
+					"total", format.HumanBytes2(gpu.TotalMemory),
+					"free", format.HumanBytes2(gpu.FreeMemory),
+				),
+				slog.Group(
+					"now",
+					"total", format.HumanBytes2(uint64(memInfo.total)),
+					"free", format.HumanBytes2(uint64(memInfo.free)),
+					"used", format.HumanBytes2(uint64(memInfo.used)),
+				),
+			)
+			cudaGPUs[i].FreeMemory = uint64(memInfo.free)
+		}
 
-			// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
-			resp = append(resp, gpuInfo)
+		if oHandles == nil && len(oneapiGPUs) > 0 {
+			oHandles = initOneAPIHandles()
+		}
+		for i, gpu := range oneapiGPUs {
+			if oHandles.oneapi == nil {
+				// shouldn't happen
+				slog.Warn("nil oneapi handle with device count", "count", oHandles.deviceCount)
+				continue
+			}
+			C.oneapi_check_vram(*oHandles.oneapi, C.int(gpu.driverIndex), C.int(gpu.gpuIndex), &memInfo)
+			// TODO - convert this to MinimumMemory based on testing...
+			var totalFreeMem float64 = float64(memInfo.free) * 0.95 // work-around: leave some reserve vram for mkl lib used in ggml-sycl backend.
+			memInfo.free = C.uint64_t(totalFreeMem)
+			oneapiGPUs[i].FreeMemory = uint64(memInfo.free)
+		}
+
+		err = RocmGPUInfoList(rocmGPUs).RefreshFreeMemory()
+		if err != nil {
+			slog.Debug("problem refreshing ROCm free memory", "error", err)
 		}
 	}
 
-	// Then AMD
-	resp = append(resp, AMDGetGPUInfo()...)
-
+	resp := []GpuInfo{}
+	for _, gpu := range cudaGPUs {
+		resp = append(resp, gpu.GpuInfo)
+	}
+	for _, gpu := range rocmGPUs {
+		resp = append(resp, gpu.GpuInfo)
+	}
+	for _, gpu := range oneapiGPUs {
+		resp = append(resp, gpu.GpuInfo)
+	}
 	if len(resp) == 0 {
-		C.cpu_check_ram(&memInfo)
-		if memInfo.err != nil {
-			slog.Info("error looking up CPU memory", "error", C.GoString(memInfo.err))
-			C.free(unsafe.Pointer(memInfo.err))
-			return resp
-		}
-		gpuInfo := GpuInfo{
-			Library: "cpu",
-			Variant: cpuVariant,
-		}
-		gpuInfo.TotalMemory = uint64(memInfo.total)
-		gpuInfo.FreeMemory = uint64(memInfo.free)
-		gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
-
-		resp = append(resp, gpuInfo)
+		resp = append(resp, cpus[0].GpuInfo)
 	}
-
 	return resp
 }
 
-func GetCPUMem() (memInfo, error) {
-	var ret memInfo
-	var info C.mem_info_t
-	C.cpu_check_ram(&info)
-	if info.err != nil {
-		defer C.free(unsafe.Pointer(info.err))
-		return ret, fmt.Errorf(C.GoString(info.err))
-	}
-	ret.FreeMemory = uint64(info.free)
-	ret.TotalMemory = uint64(info.total)
-	return ret, nil
-}
-
 func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	// Multiple GPU libraries may exist, and some may not work, so keep trying until we exhaust them
 	var ldPaths []string
@@ -362,8 +523,26 @@ func LoadNVCUDAMgmt(nvcudaLibPaths []string) (int, *C.nvcuda_handle_t, string) {
 	return 0, nil, ""
 }
 
+func LoadNVMLMgmt(nvmlLibPaths []string) (*C.nvml_handle_t, string) {
+	var resp C.nvml_init_resp_t
+	resp.ch.verbose = getVerboseState()
+	for _, libPath := range nvmlLibPaths {
+		lib := C.CString(libPath)
+		defer C.free(unsafe.Pointer(lib))
+		C.nvml_init(lib, &resp)
+		if resp.err != nil {
+			slog.Info(fmt.Sprintf("Unable to load NVML management library %s: %s", libPath, C.GoString(resp.err)))
+			C.free(unsafe.Pointer(resp.err))
+		} else {
+			return &resp.ch, libPath
+		}
+	}
+	return nil, ""
+}
+
 func LoadOneapiMgmt(oneapiLibPaths []string) (int, *C.oneapi_handle_t, string) {
 	var resp C.oneapi_init_resp_t
+	num_devices := 0
 	resp.oh.verbose = getVerboseState()
 	for _, libPath := range oneapiLibPaths {
 		lib := C.CString(libPath)
@@ -373,7 +552,10 @@ func LoadOneapiMgmt(oneapiLibPaths []string) (int, *C.oneapi_handle_t, string) {
 			slog.Debug("Unable to load oneAPI management library", "library", libPath, "error", C.GoString(resp.err))
 			C.free(unsafe.Pointer(resp.err))
 		} else {
-			return int(resp.num_devices), &resp.oh, libPath
+			for i := range resp.oh.num_drivers {
+				num_devices += int(C.oneapi_get_device_count(resp.oh, C.int(i)))
+			}
+			return num_devices, &resp.oh, libPath
 		}
 	}
 	return 0, nil, ""

gpu/gpu_darwin.go

@@ -24,7 +24,7 @@ func GetGPUInfo() GpuInfoList {
 		return []GpuInfo{
 			{
 				Library: "cpu",
-				Variant: GetCPUVariant(),
+				Variant: GetCPUCapability(),
 				memInfo: mem,
 			},
 		}
@@ -42,6 +42,17 @@ func GetGPUInfo() GpuInfoList {
 	return []GpuInfo{info}
 }
 
+func GetCPUInfo() GpuInfoList {
+	mem, _ := GetCPUMem()
+	return []GpuInfo{
+		{
+			Library: "cpu",
+			Variant: GetCPUCapability(),
+			memInfo: mem,
+		},
+	}
+}
+
 func GetCPUMem() (memInfo, error) {
 	return memInfo{
 		TotalMemory: uint64(C.getPhysicalMemory()),

gpu/gpu_info.h

@@ -47,6 +47,7 @@ typedef struct mem_info {
   char gpu_name[GPU_NAME_LEN];
   uint64_t total;
   uint64_t free;
+  uint64_t used;
 
   // Compute Capability
   int major; 
@@ -62,6 +63,7 @@ void cpu_check_ram(mem_info_t *resp);
 
 #include "gpu_info_cudart.h"
 #include "gpu_info_nvcuda.h"
+#include "gpu_info_nvml.h"
 #include "gpu_info_oneapi.h"
 
 #endif  // __GPU_INFO_H__

gpu/gpu_info_cpu.c

@@ -1,45 +0,0 @@
-#include "gpu_info.h"
-// Fallbacks for CPU mode
-
-#ifdef _WIN32
-#include <sysinfoapi.h>
-void cpu_check_ram(mem_info_t *resp) {
-  resp->err = NULL;
-  MEMORYSTATUSEX info;
-  info.dwLength = sizeof(info);
-  if (GlobalMemoryStatusEx(&info) != 0) {
-    resp->total = info.ullTotalPhys;
-    resp->free = info.ullAvailPhys;
-    snprintf(&resp->gpu_id[0], GPU_ID_LEN, "0");
-  } else {
-    resp->err = LOAD_ERR();
-  }
-  return;
-}
-
-#elif __linux__
-#include <errno.h>
-#include <string.h>
-#include <sys/sysinfo.h>
-void cpu_check_ram(mem_info_t *resp) {
-  struct sysinfo info;
-  resp->err = NULL;
-  if (sysinfo(&info) != 0) {
-    resp->err = strdup(strerror(errno));
-  } else {
-    resp->total = info.totalram * info.mem_unit;
-    resp->free = info.freeram * info.mem_unit;
-    snprintf(&resp->gpu_id[0], GPU_ID_LEN, "0");
-  }
-  return;
-}
-
-#elif __APPLE__
-// TODO consider an Apple implementation that does something useful
-// mem_info_t cpu_check_ram() {
-//   mem_info_t resp = {0, 0, NULL};
-//   return resp;
-// }
-#else
-#error "Unsupported platform"
-#endif

gpu/gpu_info_cudart.c

@@ -40,7 +40,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
 
   for (i = 0; l[i].s != NULL; i++) {
     *l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
-    if (!l[i].p) {
+    if (!*(l[i].p)) {
       char *msg = LOAD_ERR();
       LOG(resp->ch.verbose, "dlerr: %s\n", msg);
       UNLOAD_LIBRARY(resp->ch.handle);
@@ -94,7 +94,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
 }
 
 
-void cudart_check_vram(cudart_handle_t h, int i, mem_info_t *resp) {
+void cudart_bootstrap(cudart_handle_t h, int i, mem_info_t *resp) {
   resp->err = NULL;
   cudartMemory_t memInfo = {0,0,0};
   cudartReturn_t ret;
@@ -166,9 +166,11 @@ void cudart_check_vram(cudart_handle_t h, int i, mem_info_t *resp) {
 
   resp->total = memInfo.total;
   resp->free = memInfo.free;
+  resp->used = memInfo.used;
 
   LOG(h.verbose, "[%s] CUDA totalMem %lu\n", resp->gpu_id, resp->total);
   LOG(h.verbose, "[%s] CUDA freeMem %lu\n", resp->gpu_id, resp->free);
+  LOG(h.verbose, "[%s] CUDA usedMem %lu\n", resp->gpu_id, resp->used);
   LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
 }
 

gpu/gpu_info_cudart.h

@@ -140,7 +140,8 @@ typedef struct cudart_init_resp {
 } cudart_init_resp_t;
 
 void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp);
-void cudart_check_vram(cudart_handle_t ch, int device_id, mem_info_t *resp);
+void cudart_bootstrap(cudart_handle_t ch, int device_id, mem_info_t *resp);
+// TODO - if we keep this library longer term, add cudart_get_free
 void cudart_release(cudart_handle_t ch);
 
 #endif  // __GPU_INFO_CUDART_H__

gpu/gpu_info_nvcuda.c

@@ -43,7 +43,7 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
 
   for (i = 0; l[i].s != NULL; i++) {
     *l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
-    if (!*l[i].p) {
+    if (!*(l[i].p)) {
       char *msg = LOAD_ERR();
       LOG(resp->ch.verbose, "dlerr: %s\n", msg);
       UNLOAD_LIBRARY(resp->ch.handle);
@@ -96,7 +96,7 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
 }
 
 const int buflen = 256;
-void nvcuda_check_vram(nvcuda_handle_t h, int i, mem_info_t *resp) {
+void nvcuda_bootstrap(nvcuda_handle_t h, int i, mem_info_t *resp) {
   resp->err = NULL;
   nvcudaMemory_t memInfo = {0,0};
   CUresult ret;
@@ -168,7 +168,7 @@ void nvcuda_check_vram(nvcuda_handle_t h, int i, mem_info_t *resp) {
   // To get memory we have to set (and release) a context
   ret = (*h.cuCtxCreate_v3)(&ctx, NULL, 0, 0, device);
   if (ret != CUDA_SUCCESS) {
-    snprintf(buf, buflen, "nvcuda failed to get primary device context %d", ret);
+    snprintf(buf, buflen, "nvcuda failed to get device context %d", ret);
     resp->err = strdup(buf);
     return;
   }
@@ -193,7 +193,42 @@ void nvcuda_check_vram(nvcuda_handle_t h, int i, mem_info_t *resp) {
 
   ret = (*h.cuCtxDestroy)(ctx);
   if (ret != CUDA_SUCCESS) {
-    LOG(1, "nvcuda failed to release primary device context %d", ret);
+    LOG(1, "nvcuda failed to release device context %d", ret);
+  }
+}
+
+void nvcuda_get_free(nvcuda_handle_t h, int i, uint64_t *free, uint64_t *total) {
+  CUresult ret;
+  CUcontext ctx = NULL;
+  CUdevice device = -1;
+  *free = 0;
+  *total = 0;
+
+  ret = (*h.cuDeviceGet)(&device, i);
+  if (ret != CUDA_SUCCESS) {
+    LOG(1, "nvcuda device failed to initialize");
+    return;
+  }
+
+
+  // To get memory we have to set (and release) a context
+  ret = (*h.cuCtxCreate_v3)(&ctx, NULL, 0, 0, device);
+  if (ret != CUDA_SUCCESS) {
+    LOG(1, "nvcuda failed to get device context %d", ret);
+    return;
+  }
+
+  ret = (*h.cuMemGetInfo_v2)(free, total);
+  if (ret != CUDA_SUCCESS) {
+    LOG(1, "nvcuda device memory info lookup failure %d", ret);
+    // Best effort on failure...
+    (*h.cuCtxDestroy)(ctx);
+    return;
+  }
+
+  ret = (*h.cuCtxDestroy)(ctx);
+  if (ret != CUDA_SUCCESS) {
+    LOG(1, "nvcuda failed to release device context %d", ret);
   }
 }
 

gpu/gpu_info_nvcuda.h

@@ -67,7 +67,8 @@ typedef struct nvcuda_init_resp {
 } nvcuda_init_resp_t;
 
 void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp);
-void nvcuda_check_vram(nvcuda_handle_t ch, int device_id, mem_info_t *resp);
+void nvcuda_bootstrap(nvcuda_handle_t ch, int device_id, mem_info_t *resp);
+void nvcuda_get_free(nvcuda_handle_t ch,  int device_id, uint64_t *free, uint64_t *total);
 void nvcuda_release(nvcuda_handle_t ch);
 
 #endif  // __GPU_INFO_NVCUDA_H__

gpu/gpu_info_nvml.c

@@ -0,0 +1,104 @@
+#ifndef __APPLE__  // TODO - maybe consider nvidia support on intel macs?
+
+#include <string.h>
+
+#include "gpu_info_nvml.h"
+
+void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp) {
+  nvmlReturn_t ret;
+  resp->err = NULL;
+  const int buflen = 256;
+  char buf[buflen + 1];
+  int i;
+
+  struct lookup {
+    char *s;
+    void **p;
+  } l[] = {
+      {"nvmlInit_v2", (void *)&resp->ch.nvmlInit_v2},
+      {"nvmlShutdown", (void *)&resp->ch.nvmlShutdown},
+      {"nvmlDeviceGetHandleByIndex", (void *)&resp->ch.nvmlDeviceGetHandleByIndex},
+      {"nvmlDeviceGetMemoryInfo", (void *)&resp->ch.nvmlDeviceGetMemoryInfo},
+      {NULL, NULL},
+  };
+
+  resp->ch.handle = LOAD_LIBRARY(nvml_lib_path, RTLD_LAZY);
+  if (!resp->ch.handle) {
+    char *msg = LOAD_ERR();
+    LOG(resp->ch.verbose, "library %s load err: %s\n", nvml_lib_path, msg);
+    snprintf(buf, buflen,
+             "Unable to load %s library to query for Nvidia GPUs: %s",
+             nvml_lib_path, msg);
+    free(msg);
+    resp->err = strdup(buf);
+    return;
+  }
+
+  // TODO once we've squashed the remaining corner cases remove this log
+  // LOG(resp->ch.verbose, "wiring nvidia management library functions in %s\n", nvml_lib_path);
+  
+  for (i = 0; l[i].s != NULL; i++) {
+    // TODO once we've squashed the remaining corner cases remove this log
+    // LOG(resp->ch.verbose, "dlsym: %s\n", l[i].s);
+
+    *l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
+    if (!*(l[i].p)) {
+      resp->ch.handle = NULL;
+      char *msg = LOAD_ERR();
+      LOG(resp->ch.verbose, "dlerr: %s\n", msg);
+      UNLOAD_LIBRARY(resp->ch.handle);
+      snprintf(buf, buflen, "symbol lookup for %s failed: %s", l[i].s,
+               msg);
+      free(msg);
+      resp->err = strdup(buf);
+      return;
+    }
+  }
+
+  ret = (*resp->ch.nvmlInit_v2)();
+  if (ret != NVML_SUCCESS) {
+    LOG(resp->ch.verbose, "nvmlInit_v2 err: %d\n", ret);
+    UNLOAD_LIBRARY(resp->ch.handle);
+    resp->ch.handle = NULL;
+    snprintf(buf, buflen, "nvml vram init failure: %d", ret);
+    resp->err = strdup(buf);
+    return;
+  }
+}
+
+
+void nvml_get_free(nvml_handle_t h, int device_id, uint64_t *free, uint64_t *total, uint64_t *used) {
+    nvmlDevice_t device;
+    nvmlMemory_t memInfo = {0};
+    nvmlReturn_t ret;
+    ret = (*h.nvmlDeviceGetHandleByIndex)(device_id, &device);
+    if (ret != NVML_SUCCESS) {
+        LOG(1, "unable to get device handle %d: %d", device_id, ret);
+        *free = 0;
+        return;
+    }
+
+    ret = (*h.nvmlDeviceGetMemoryInfo)(device, &memInfo);
+    if (ret != NVML_SUCCESS) {
+        LOG(1, "device memory info lookup failure %d: %d", device_id, ret);
+        *free = 0;
+        return;
+    }
+    *free = memInfo.free;
+    *total = memInfo.total;
+    *used = memInfo.used;
+}
+
+
+void nvml_release(nvml_handle_t h) {
+  LOG(h.verbose, "releasing nvml library\n");
+  nvmlReturn_t ret;
+  ret = (*h.nvmlShutdown)();
+  if (ret != NVML_SUCCESS) {
+    LOG(1, "error during nvmlShutdown %d", ret);
+  }
+  UNLOAD_LIBRARY(h.handle);
+  h.handle = NULL;
+}
+
+#endif  // __APPLE__

gpu/gpu_info_nvml.h

@@ -0,0 +1,48 @@
+#ifndef __APPLE__
+#ifndef __GPU_INFO_NVML_H__
+#define __GPU_INFO_NVML_H__
+#include "gpu_info.h"
+
+// Just enough typedef's to dlopen/dlsym for memory information
+typedef enum nvmlReturn_enum {
+  NVML_SUCCESS = 0,
+  // Other values omitted for now...
+} nvmlReturn_t;
+typedef void *nvmlDevice_t;  // Opaque is sufficient
+typedef struct nvmlMemory_st {
+  unsigned long long total;
+  unsigned long long free;
+  unsigned long long used;
+} nvmlMemory_t;
+
+typedef enum nvmlBrandType_enum
+{
+    NVML_BRAND_UNKNOWN          = 0,
+} nvmlBrandType_t;
+
+typedef struct nvml_handle {
+  void *handle;
+  uint16_t verbose;
+  nvmlReturn_t (*nvmlInit_v2)(void);
+  nvmlReturn_t (*nvmlShutdown)(void);
+  nvmlReturn_t (*nvmlDeviceGetHandleByIndex)(unsigned int, nvmlDevice_t *);
+  nvmlReturn_t (*nvmlDeviceGetMemoryInfo)(nvmlDevice_t, nvmlMemory_t *);
+} nvml_handle_t;
+
+typedef struct nvml_init_resp {
+  char *err;  // If err is non-null handle is invalid
+  nvml_handle_t ch;
+} nvml_init_resp_t;
+
+typedef struct nvml_compute_capability {
+  char *err;
+  int major;
+  int minor;
+} nvml_compute_capability_t;
+
+void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp);
+void nvml_get_free(nvml_handle_t ch,  int device_id, uint64_t *free, uint64_t *total, uint64_t *used);
+void nvml_release(nvml_handle_t ch);
+
+#endif  // __GPU_INFO_NVML_H__
+#endif  // __APPLE__

gpu/gpu_info_oneapi.c

@@ -4,15 +4,17 @@
 
 #include <string.h>
 
-void oneapi_init(char *oneapi_lib_path, oneapi_init_resp_t *resp)
-{
+void oneapi_init(char *oneapi_lib_path, oneapi_init_resp_t *resp) {
   ze_result_t ret;
   resp->err = NULL;
+  resp->oh.devices = NULL;
+  resp->oh.num_devices = NULL;
+  resp->oh.drivers = NULL;
+  resp->oh.num_drivers = 0;
   const int buflen = 256;
   char buf[buflen + 1];
-  int i;
-  struct lookup
-  {
+  int i, d;
+  struct lookup {
     char *s;
     void **p;
   } l[] = {
@@ -28,8 +30,7 @@ void oneapi_init(char *oneapi_lib_path, oneapi_init_resp_t *resp)
   };
 
   resp->oh.handle = LOAD_LIBRARY(oneapi_lib_path, RTLD_LAZY);
-  if (!resp->oh.handle)
-  {
+  if (!resp->oh.handle) {
     char *msg = LOAD_ERR();
     snprintf(buf, buflen,
              "Unable to load %s library to query for Intel GPUs: %s\n",
@@ -44,14 +45,12 @@ void oneapi_init(char *oneapi_lib_path, oneapi_init_resp_t *resp)
       "wiring Level-Zero management library functions in %s\n",
       oneapi_lib_path);
 
-  for (i = 0; l[i].s != NULL; i++)
-  {
+  for (i = 0; l[i].s != NULL; i++) {
     // TODO once we've squashed the remaining corner cases remove this log
     LOG(resp->oh.verbose, "dlsym: %s\n", l[i].s);
 
     *l[i].p = LOAD_SYMBOL(resp->oh.handle, l[i].s);
-    if (!l[i].p)
-    {
+    if (!*(l[i].p)) {
       resp->oh.handle = NULL;
       char *msg = LOAD_ERR();
       LOG(resp->oh.verbose, "dlerr: %s\n", msg);
@@ -63,23 +62,70 @@ void oneapi_init(char *oneapi_lib_path, oneapi_init_resp_t *resp)
     }
   }
 
+  LOG(resp->oh.verbose, "calling zesInit\n");
+
   ret = (*resp->oh.zesInit)(0);
-  if (ret != ZE_RESULT_SUCCESS)
-  {
-    LOG(resp->oh.verbose, "zesInit err: %d\n", ret);
-    UNLOAD_LIBRARY(resp->oh.handle);
-    resp->oh.handle = NULL;
-    snprintf(buf, buflen, "oneapi vram init failure: %d", ret);
+  if (ret != ZE_RESULT_SUCCESS) {
+    LOG(resp->oh.verbose, "zesInit err: %x\n", ret);
+    snprintf(buf, buflen, "oneapi vram init failure: %x", ret);
     resp->err = strdup(buf);
+    oneapi_release(resp->oh);
+    return;
   }
 
-  (*resp->oh.zesDriverGet)(&resp->num_devices, NULL);
+  LOG(resp->oh.verbose, "calling zesDriverGet\n");
+  ret = (*resp->oh.zesDriverGet)(&resp->oh.num_drivers, NULL);
+  if (ret != ZE_RESULT_SUCCESS) {
+    LOG(resp->oh.verbose, "zesDriverGet err: %x\n", ret);
+    snprintf(buf, buflen, "unable to get driver count: %x", ret);
+    resp->err = strdup(buf);
+    oneapi_release(resp->oh);
+    return;
+  }
+  LOG(resp->oh.verbose, "oneapi driver count: %d\n", resp->oh.num_drivers);
+  resp->oh.drivers = malloc(resp->oh.num_drivers * sizeof(zes_driver_handle_t));
+  resp->oh.num_devices = malloc(resp->oh.num_drivers * sizeof(uint32_t));
+  memset(&resp->oh.num_devices[0], 0, resp->oh.num_drivers * sizeof(uint32_t));
+  resp->oh.devices =
+      malloc(resp->oh.num_drivers * sizeof(zes_device_handle_t *));
+  ret = (*resp->oh.zesDriverGet)(&resp->oh.num_drivers, &resp->oh.drivers[0]);
+  if (ret != ZE_RESULT_SUCCESS) {
+    LOG(resp->oh.verbose, "zesDriverGet err: %x\n", ret);
+    snprintf(buf, buflen, "unable to get driver count: %x", ret);
+    resp->err = strdup(buf);
+    oneapi_release(resp->oh);
+    return;
+  }
+
+  for (d = 0; d < resp->oh.num_drivers; d++) {
+    LOG(resp->oh.verbose, "calling zesDeviceGet count %d: %p\n", d, resp->oh.drivers[d]);
+    ret = (*resp->oh.zesDeviceGet)(resp->oh.drivers[d],
+                                   &resp->oh.num_devices[d], NULL);
+    if (ret != ZE_RESULT_SUCCESS) {
+      LOG(resp->oh.verbose, "zesDeviceGet err: %x\n", ret);
+      snprintf(buf, buflen, "unable to get device count: %x", ret);
+      resp->err = strdup(buf);
+      oneapi_release(resp->oh);
+      return;
+    }
+    resp->oh.devices[d] =
+        malloc(resp->oh.num_devices[d] * sizeof(zes_device_handle_t));
+    ret = (*resp->oh.zesDeviceGet)(
+        resp->oh.drivers[d], &resp->oh.num_devices[d], resp->oh.devices[d]);
+    if (ret != ZE_RESULT_SUCCESS) {
+      LOG(resp->oh.verbose, "zesDeviceGet err: %x\n", ret);
+      snprintf(buf, buflen, "unable to get device count: %x", ret);
+      resp->err = strdup(buf);
+      oneapi_release(resp->oh);
+      return;
+    }
+  }
 
   return;
 }
 
-void oneapi_check_vram(oneapi_handle_t h, mem_info_t *resp)
-{
+void oneapi_check_vram(oneapi_handle_t h, int driver, int device,
+                       mem_info_t *resp) {
   ze_result_t ret;
   resp->err = NULL;
   uint64_t totalMem = 0;
@@ -88,127 +134,126 @@ void oneapi_check_vram(oneapi_handle_t h, mem_info_t *resp)
   char buf[buflen + 1];
   int i, d, m;
 
-  if (h.handle == NULL)
-  {
+  if (h.handle == NULL) {
     resp->err = strdup("Level-Zero handle not initialized");
     return;
   }
 
-  uint32_t driversCount = 0;
-  ret = (*h.zesDriverGet)(&driversCount, NULL);
-  if (ret != ZE_RESULT_SUCCESS)
-  {
-    snprintf(buf, buflen, "unable to get driver count: %d", ret);
-    resp->err = strdup(buf);
+  if (driver > h.num_drivers || device > h.num_devices[driver]) {
+    resp->err = strdup("driver of device index out of bounds");
     return;
   }
-  LOG(h.verbose, "discovered %d Level-Zero drivers\n", driversCount);
-
-  zes_driver_handle_t *allDrivers =
-      malloc(driversCount * sizeof(zes_driver_handle_t));
-  (*h.zesDriverGet)(&driversCount, allDrivers);
 
   resp->total = 0;
   resp->free = 0;
 
-  for (d = 0; d < driversCount; d++)
-  {
-    uint32_t deviceCount = 0;
-    ret = (*h.zesDeviceGet)(allDrivers[d], &deviceCount, NULL);
-    if (ret != ZE_RESULT_SUCCESS)
-    {
-      snprintf(buf, buflen, "unable to get device count: %d", ret);
+  zes_device_ext_properties_t ext_props;
+  ext_props.stype = ZES_STRUCTURE_TYPE_DEVICE_EXT_PROPERTIES;
+  ext_props.pNext = NULL;
+
+  zes_device_properties_t props;
+  props.stype = ZES_STRUCTURE_TYPE_DEVICE_PROPERTIES;
+  props.pNext = &ext_props;
+
+  ret = (*h.zesDeviceGetProperties)(h.devices[driver][device], &props);
+  if (ret != ZE_RESULT_SUCCESS) {
+    snprintf(buf, buflen, "unable to get device properties: %d", ret);
+    resp->err = strdup(buf);
+    return;
+  }
+
+  snprintf(&resp->gpu_name[0], GPU_NAME_LEN, "%s", props.modelName);
+
+  // TODO this needs to map to ONEAPI_DEVICE_SELECTOR syntax
+  // (this is probably wrong...)
+  // TODO - the driver isn't included - what if there are multiple drivers?
+  snprintf(&resp->gpu_id[0], GPU_ID_LEN, "%d", device);
+
+  if (h.verbose) {
+    // When in verbose mode, report more information about
+    // the card we discover.
+    LOG(h.verbose, "[%d:%d] oneAPI device name: %s\n", driver, device,
+        props.modelName);
+    LOG(h.verbose, "[%d:%d] oneAPI brand: %s\n", driver, device,
+        props.brandName);
+    LOG(h.verbose, "[%d:%d] oneAPI vendor: %s\n", driver, device,
+        props.vendorName);
+    LOG(h.verbose, "[%d:%d] oneAPI S/N: %s\n", driver, device,
+        props.serialNumber);
+    LOG(h.verbose, "[%d:%d] oneAPI board number: %s\n", driver, device,
+        props.boardNumber);
+  }
+
+  // TODO
+  // Compute Capability equivalent in resp->major, resp->minor, resp->patch
+
+  uint32_t memCount = 0;
+  ret = (*h.zesDeviceEnumMemoryModules)(h.devices[driver][device], &memCount,
+                                        NULL);
+  if (ret != ZE_RESULT_SUCCESS) {
+    snprintf(buf, buflen, "unable to enumerate Level-Zero memory modules: %x",
+             ret);
+    resp->err = strdup(buf);
+    return;
+  }
+
+  LOG(h.verbose, "discovered %d Level-Zero memory modules\n", memCount);
+
+  zes_mem_handle_t *mems = malloc(memCount * sizeof(zes_mem_handle_t));
+  (*h.zesDeviceEnumMemoryModules)(h.devices[driver][device], &memCount, mems);
+
+  for (m = 0; m < memCount; m++) {
+    zes_mem_state_t state;
+    state.stype = ZES_STRUCTURE_TYPE_MEM_STATE;
+    state.pNext = NULL;
+    ret = (*h.zesMemoryGetState)(mems[m], &state);
+    if (ret != ZE_RESULT_SUCCESS) {
+      snprintf(buf, buflen, "unable to get memory state: %x", ret);
       resp->err = strdup(buf);
-      free(allDrivers);
+      free(mems);
       return;
     }
 
-    LOG(h.verbose, "discovered %d Level-Zero devices\n", deviceCount);
-
-    zes_device_handle_t *devices =
-        malloc(deviceCount * sizeof(zes_device_handle_t));
-    (*h.zesDeviceGet)(allDrivers[d], &deviceCount, devices);
-
-    for (i = 0; i < deviceCount; i++)
-    {
-      zes_device_ext_properties_t ext_props;
-      ext_props.stype = ZES_STRUCTURE_TYPE_DEVICE_EXT_PROPERTIES;
-      ext_props.pNext = NULL;
-
-      zes_device_properties_t props;
-      props.stype = ZES_STRUCTURE_TYPE_DEVICE_PROPERTIES;
-      props.pNext = &ext_props;
-
-      ret = (*h.zesDeviceGetProperties)(devices[i], &props);
-      if (ret != ZE_RESULT_SUCCESS)
-      {
-        snprintf(buf, buflen, "unable to get device properties: %d", ret);
-        resp->err = strdup(buf);
-        free(allDrivers);
-        free(devices);
-        return;
-      }
-
-      if (h.verbose)
-      {
-        // When in verbose mode, report more information about
-        // the card we discover.
-        LOG(h.verbose, "[%d] oneAPI device name: %s\n", i,
-            props.modelName);
-        LOG(h.verbose, "[%d] oneAPI brand: %s\n", i,
-            props.brandName);
-        LOG(h.verbose, "[%d] oneAPI vendor: %s\n", i,
-            props.vendorName);
-        LOG(h.verbose, "[%d] oneAPI S/N: %s\n", i,
-            props.serialNumber);
-        LOG(h.verbose, "[%d] oneAPI board number: %s\n", i,
-            props.boardNumber);
-      }
-
-      uint32_t memCount = 0;
-      ret = (*h.zesDeviceEnumMemoryModules)(devices[i], &memCount, NULL);
-      if (ret != ZE_RESULT_SUCCESS)
-      {
-        snprintf(buf, buflen,
-                 "unable to enumerate Level-Zero memory modules: %d", ret);
-        resp->err = strdup(buf);
-        free(allDrivers);
-        free(devices);
-        return;
-      }
-
-      LOG(h.verbose, "discovered %d Level-Zero memory modules\n", memCount);
-
-      zes_mem_handle_t *mems = malloc(memCount * sizeof(zes_mem_handle_t));
-      (*h.zesDeviceEnumMemoryModules)(devices[i], &memCount, mems);
-
-      for (m = 0; m < memCount; m++)
-      {
-        zes_mem_state_t state;
-        state.stype = ZES_STRUCTURE_TYPE_MEM_STATE;
-        state.pNext = NULL;
-        ret = (*h.zesMemoryGetState)(mems[m], &state);
-        if (ret != ZE_RESULT_SUCCESS)
-        {
-          snprintf(buf, buflen, "unable to get memory state: %d", ret);
-          resp->err = strdup(buf);
-          free(allDrivers);
-          free(devices);
-          free(mems);
-          return;
-        }
-
-        resp->total += state.size;
-        resp->free += state.free;
-      }
-
-      free(mems);
-    }
-
-    free(devices);
+    resp->total += state.size;
+    resp->free += state.free;
   }
 
-  free(allDrivers);
+  free(mems);
+}
+
+void oneapi_release(oneapi_handle_t h) {
+  int d;
+  LOG(h.verbose, "releasing oneapi library\n");
+  for (d = 0; d < h.num_drivers; d++) {
+    if (h.devices != NULL && h.devices[d] != NULL) {
+      free(h.devices[d]);
+    }
+  }
+  if (h.devices != NULL) {
+    free(h.devices);
+    h.devices = NULL;
+  }
+  if (h.num_devices != NULL) {
+    free(h.num_devices);
+    h.num_devices = NULL;
+  }
+  if (h.drivers != NULL) {
+    free(h.drivers);
+    h.drivers = NULL;
+  }
+  h.num_drivers = 0;
+  UNLOAD_LIBRARY(h.handle);
+  h.handle = NULL;
+}
+
+int oneapi_get_device_count(oneapi_handle_t h, int driver) {
+  if (h.handle == NULL || h.num_devices == NULL) {
+    return 0;
+  }
+  if (driver > h.num_drivers) {
+    return 0;
+  }
+  return (int)h.num_devices[driver];
 }
 
 #endif // __APPLE__

gpu/gpu_info_oneapi.h

@@ -9,8 +9,7 @@
 #define ZE_BIT(_i) (1 << _i)
 
 // Just enough typedef's to dlopen/dlsym for memory information
-typedef enum ze_result_t
-{
+typedef enum ze_result_t {
   ZE_RESULT_SUCCESS = 0,
   // Other values omitted for now...
 } ze_result_t;
@@ -20,13 +19,11 @@ typedef struct _zes_driver_handle_t *zes_driver_handle_t;
 typedef struct _zes_device_handle_t *zes_device_handle_t;
 typedef struct _zes_mem_handle_t *zes_mem_handle_t;
 
-typedef enum _ze_structure_type_t
-{
+typedef enum _ze_structure_type_t {
   ZE_STRUCTURE_TYPE_FORCE_UINT32 = 0x7fffffff
 } ze_structure_type_t;
 
-typedef enum _zes_structure_type_t
-{
+typedef enum _zes_structure_type_t {
   ZES_STRUCTURE_TYPE_DEVICE_PROPERTIES = 0x1,
   ZES_STRUCTURE_TYPE_MEM_PROPERTIES = 0xb,
   ZES_STRUCTURE_TYPE_MEM_STATE = 0x1e,
@@ -34,35 +31,29 @@ typedef enum _zes_structure_type_t
   ZES_STRUCTURE_TYPE_FORCE_UINT32 = 0x7fffffff
 } zes_structure_type_t;
 
-typedef enum _zes_mem_type_t
-{
+typedef enum _zes_mem_type_t {
   ZES_MEM_TYPE_FORCE_UINT32 = 0x7fffffff
 } zes_mem_type_t;
 
-typedef enum _zes_mem_loc_t
-{
+typedef enum _zes_mem_loc_t {
   ZES_MEM_LOC_SYSTEM = 0,
   ZES_MEM_LOC_DEVICE = 1,
   ZES_MEM_LOC_FORCE_UINT32 = 0x7fffffff
 } zes_mem_loc_t;
 
-typedef enum _zes_mem_health_t
-{
+typedef enum _zes_mem_health_t {
   ZES_MEM_HEALTH_FORCE_UINT32 = 0x7fffffff
 } zes_mem_health_t;
 
-typedef struct _ze_device_uuid_t
-{
+typedef struct _ze_device_uuid_t {
   uint8_t id[ZE_MAX_DEVICE_UUID_SIZE];
 } ze_device_uuid_t;
 
-typedef struct _zes_uuid_t
-{
+typedef struct _zes_uuid_t {
   uint8_t id[ZE_MAX_DEVICE_UUID_SIZE];
 } zes_uuid_t;
 
-typedef enum _ze_device_type_t
-{
+typedef enum _ze_device_type_t {
   ZE_DEVICE_TYPE_GPU = 1,
   ZE_DEVICE_TYPE_CPU = 2,
   ZE_DEVICE_TYPE_FPGA = 3,
@@ -71,8 +62,7 @@ typedef enum _ze_device_type_t
   ZE_DEVICE_TYPE_FORCE_UINT32 = 0x7fffffff
 } ze_device_type_t;
 
-typedef enum _zes_device_type_t
-{
+typedef enum _zes_device_type_t {
   ZES_DEVICE_TYPE_GPU = 1,
   ZES_DEVICE_TYPE_CPU = 2,
   ZES_DEVICE_TYPE_FPGA = 3,
@@ -82,8 +72,7 @@ typedef enum _zes_device_type_t
 } zes_device_type_t;
 
 typedef uint32_t ze_device_property_flags_t;
-typedef enum _ze_device_property_flag_t
-{
+typedef enum _ze_device_property_flag_t {
   ZE_DEVICE_PROPERTY_FLAG_INTEGRATED = ZE_BIT(0),
   ZE_DEVICE_PROPERTY_FLAG_SUBDEVICE = ZE_BIT(1),
   ZE_DEVICE_PROPERTY_FLAG_ECC = ZE_BIT(2),
@@ -92,8 +81,7 @@ typedef enum _ze_device_property_flag_t
 } ze_device_property_flag_t;
 
 typedef uint32_t zes_device_property_flags_t;
-typedef enum _zes_device_property_flag_t
-{
+typedef enum _zes_device_property_flag_t {
   ZES_DEVICE_PROPERTY_FLAG_INTEGRATED = ZE_BIT(0),
   ZES_DEVICE_PROPERTY_FLAG_SUBDEVICE = ZE_BIT(1),
   ZES_DEVICE_PROPERTY_FLAG_ECC = ZE_BIT(2),
@@ -101,8 +89,7 @@ typedef enum _zes_device_property_flag_t
   ZES_DEVICE_PROPERTY_FLAG_FORCE_UINT32 = 0x7fffffff
 } zes_device_property_flag_t;
 
-typedef struct _ze_device_properties_t
-{
+typedef struct _ze_device_properties_t {
   ze_structure_type_t stype;
   void *pNext;
   ze_device_type_t type;
@@ -126,8 +113,7 @@ typedef struct _ze_device_properties_t
   char name[ZE_MAX_DEVICE_NAME];
 } ze_device_properties_t;
 
-typedef struct _zes_device_properties_t
-{
+typedef struct _zes_device_properties_t {
   zes_structure_type_t stype;
   void *pNext;
   ze_device_properties_t core;
@@ -140,8 +126,7 @@ typedef struct _zes_device_properties_t
   char driverVersion[ZES_STRING_PROPERTY_SIZE];
 } zes_device_properties_t;
 
-typedef struct _zes_device_ext_properties_t
-{
+typedef struct _zes_device_ext_properties_t {
   zes_structure_type_t stype;
   void *pNext;
   zes_uuid_t uuid;
@@ -149,8 +134,7 @@ typedef struct _zes_device_ext_properties_t
   zes_device_property_flags_t flags;
 } zes_device_ext_properties_t;
 
-typedef struct _zes_mem_properties_t
-{
+typedef struct _zes_mem_properties_t {
   zes_structure_type_t stype;
   void *pNext;
   zes_mem_type_t type;
@@ -162,8 +146,7 @@ typedef struct _zes_mem_properties_t
   int32_t numChannels;
 } zes_mem_properties_t;
 
-typedef struct _zes_mem_state_t
-{
+typedef struct _zes_mem_state_t {
   zes_structure_type_t stype;
   const void *pNext;
   zes_mem_health_t health;
@@ -171,10 +154,19 @@ typedef struct _zes_mem_state_t
   uint64_t size;
 } zes_mem_state_t;
 
-typedef struct oneapi_handle
-{
+typedef struct oneapi_handle {
   void *handle;
   uint16_t verbose;
+
+  uint32_t num_drivers;
+  zes_driver_handle_t *drivers;
+  uint32_t *num_devices;
+  zes_device_handle_t **devices;
+
+  // TODO Driver major, minor information
+  // int driver_major;
+  // int driver_minor;
+
   ze_result_t (*zesInit)(int);
   ze_result_t (*zesDriverGet)(uint32_t *pCount, zes_driver_handle_t *phDrivers);
   ze_result_t (*zesDeviceGet)(zes_driver_handle_t hDriver, uint32_t *pCount,
@@ -191,21 +183,21 @@ typedef struct oneapi_handle
 
 } oneapi_handle_t;
 
-typedef struct oneapi_init_resp
-{
+typedef struct oneapi_init_resp {
   char *err; // If err is non-null handle is invalid
-  int num_devices;
   oneapi_handle_t oh;
 } oneapi_init_resp_t;
 
-typedef struct oneapi_version_resp
-{
+typedef struct oneapi_version_resp {
   ze_result_t status;
   char *str; // Contains version or error string if status != 0
 } oneapi_version_resp_t;
 
 void oneapi_init(char *oneapi_lib_path, oneapi_init_resp_t *resp);
-void oneapi_check_vram(oneapi_handle_t rh, mem_info_t *resp);
+void oneapi_check_vram(oneapi_handle_t h, int driver, int device,
+                       mem_info_t *resp);
+void oneapi_release(oneapi_handle_t h);
+int oneapi_get_device_count(oneapi_handle_t h, int driver);
 
 #endif // __GPU_INFO_INTEL_H__
 #endif // __APPLE__

gpu/gpu_linux.go

@@ -0,0 +1,89 @@
+package gpu
+
+import (
+	"bufio"
+	"fmt"
+	"os"
+	"strings"
+
+	"github.com/ollama/ollama/format"
+)
+
+var CudartGlobs = []string{
+	"/usr/local/cuda/lib64/libcudart.so*",
+	"/usr/lib/x86_64-linux-gnu/nvidia/current/libcudart.so*",
+	"/usr/lib/x86_64-linux-gnu/libcudart.so*",
+	"/usr/lib/wsl/lib/libcudart.so*",
+	"/usr/lib/wsl/drivers/*/libcudart.so*",
+	"/opt/cuda/lib64/libcudart.so*",
+	"/usr/local/cuda*/targets/aarch64-linux/lib/libcudart.so*",
+	"/usr/lib/aarch64-linux-gnu/nvidia/current/libcudart.so*",
+	"/usr/lib/aarch64-linux-gnu/libcudart.so*",
+	"/usr/local/cuda/lib*/libcudart.so*",
+	"/usr/lib*/libcudart.so*",
+	"/usr/local/lib*/libcudart.so*",
+}
+
+var NvmlGlobs = []string{}
+
+var NvcudaGlobs = []string{
+	"/usr/local/cuda*/targets/*/lib/libcuda.so*",
+	"/usr/lib/*-linux-gnu/nvidia/current/libcuda.so*",
+	"/usr/lib/*-linux-gnu/libcuda.so*",
+	"/usr/lib/wsl/lib/libcuda.so*",
+	"/usr/lib/wsl/drivers/*/libcuda.so*",
+	"/opt/cuda/lib*/libcuda.so*",
+	"/usr/local/cuda/lib*/libcuda.so*",
+	"/usr/lib*/libcuda.so*",
+	"/usr/local/lib*/libcuda.so*",
+}
+
+var OneapiGlobs = []string{
+	"/usr/lib/x86_64-linux-gnu/libze_intel_gpu.so*",
+	"/usr/lib*/libze_intel_gpu.so*",
+}
+
+var CudartMgmtName = "libcudart.so*"
+var NvcudaMgmtName = "libcuda.so*"
+var NvmlMgmtName = "" // not currently wired on linux
+var OneapiMgmtName = "libze_intel_gpu.so"
+
+func GetCPUMem() (memInfo, error) {
+	var mem memInfo
+	var total, available, free, buffers, cached uint64
+	f, err := os.Open("/proc/meminfo")
+	if err != nil {
+		return mem, err
+	}
+	defer f.Close()
+	s := bufio.NewScanner(f)
+	for s.Scan() {
+		line := s.Text()
+		switch {
+		case strings.HasPrefix(line, "MemTotal:"):
+			_, err = fmt.Sscanf(line, "MemTotal:%d", &total)
+		case strings.HasPrefix(line, "MemAvailable:"):
+			_, err = fmt.Sscanf(line, "MemAvailable:%d", &available)
+		case strings.HasPrefix(line, "MemFree:"):
+			_, err = fmt.Sscanf(line, "MemFree:%d", &free)
+		case strings.HasPrefix(line, "Buffers:"):
+			_, err = fmt.Sscanf(line, "Buffers:%d", &buffers)
+		case strings.HasPrefix(line, "Cached:"):
+			_, err = fmt.Sscanf(line, "Cached:%d", &cached)
+		default:
+			continue
+		}
+		if err != nil {
+			return mem, err
+		}
+
+		if total > 0 && available > 0 {
+			mem.TotalMemory = total * format.KibiByte
+			mem.FreeMemory = available * format.KibiByte
+			return mem, nil
+		}
+	}
+	mem.TotalMemory = total * format.KibiByte
+	mem.FreeMemory = (free + buffers + cached) * format.KibiByte
+	return mem, nil
+}

gpu/gpu_windows.go

@@ -0,0 +1,55 @@
+package gpu
+
+import (
+	"fmt"
+	"syscall"
+	"unsafe"
+)
+
+type MEMORYSTATUSEX struct {
+	length               uint32
+	MemoryLoad           uint32
+	TotalPhys            uint64
+	AvailPhys            uint64
+	TotalPageFile        uint64
+	AvailPageFile        uint64
+	TotalVirtual         uint64
+	AvailVirtual         uint64
+	AvailExtendedVirtual uint64
+}
+
+var (
+	k32                      = syscall.NewLazyDLL("kernel32.dll")
+	globalMemoryStatusExProc = k32.NewProc("GlobalMemoryStatusEx")
+	sizeofMemoryStatusEx     = uint32(unsafe.Sizeof(MEMORYSTATUSEX{}))
+)
+
+var CudartGlobs = []string{
+	"c:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v*\\bin\\cudart64_*.dll",
+}
+
+var NvmlGlobs = []string{
+	"c:\\Windows\\System32\\nvml.dll",
+}
+
+var NvcudaGlobs = []string{
+	"c:\\windows\\system*\\nvcuda.dll",
+}
+
+var OneapiGlobs = []string{
+	"c:\\Windows\\System32\\DriverStore\\FileRepository\\*\\ze_intel_gpu64.dll",
+}
+
+var CudartMgmtName = "cudart64_*.dll"
+var NvcudaMgmtName = "nvcuda.dll"
+var NvmlMgmtName = "nvml.dll"
+var OneapiMgmtName = "ze_intel_gpu64.dll"
+
+func GetCPUMem() (memInfo, error) {
+	memStatus := MEMORYSTATUSEX{length: sizeofMemoryStatusEx}
+	r1, _, err := globalMemoryStatusExProc.Call(uintptr(unsafe.Pointer(&memStatus)))
+	if r1 == 0 {
+		return memInfo{}, fmt.Errorf("GlobalMemoryStatusEx failed: %w", err)
+	}
+	return memInfo{TotalMemory: memStatus.TotalPhys, FreeMemory: memStatus.AvailPhys}, nil
+}

gpu/types.go

@@ -18,7 +18,7 @@ type GpuInfo struct {
 	Library string `json:"library,omitempty"`
 
 	// Optional variant to select (e.g. versions, cpu feature flags)
-	Variant string `json:"variant,omitempty"`
+	Variant CPUCapability `json:"variant"`
 
 	// MinimumMemory represents the minimum memory required to use the GPU
 	MinimumMemory uint64 `json:"-"`
@@ -26,6 +26,9 @@ type GpuInfo struct {
 	// Any extra PATH/LD_LIBRARY_PATH dependencies required for the Library to operate properly
 	DependencyPath string `json:"lib_path,omitempty"`
 
+	// Extra environment variables specific to the GPU as list of [key,value]
+	EnvWorkarounds [][2]string `json:"envs,omitempty"`
+
 	// GPU information
 	ID      string `json:"gpu_id"`  // string to use for selection of this specific GPU
 	Name    string `json:"name"`    // user friendly name if available
@@ -38,6 +41,30 @@ type GpuInfo struct {
 	// TODO other performance capability info to help in scheduling decisions
 }
 
+type CPUInfo struct {
+	GpuInfo
+}
+
+type CudaGPUInfo struct {
+	GpuInfo
+	index int //nolint:unused,nolintlint
+}
+type CudaGPUInfoList []CudaGPUInfo
+
+type RocmGPUInfo struct {
+	GpuInfo
+	usedFilepath string //nolint:unused,nolintlint
+	index        int    //nolint:unused,nolintlint
+}
+type RocmGPUInfoList []RocmGPUInfo
+
+type OneapiGPUInfo struct {
+	GpuInfo
+	driverIndex int //nolint:unused,nolintlint
+	gpuIndex    int //nolint:unused,nolintlint
+}
+type OneapiGPUInfoList []OneapiGPUInfo
+
 type GpuInfoList []GpuInfo
 
 // Split up the set of gpu info's by Library and variant
@@ -47,8 +74,8 @@ func (l GpuInfoList) ByLibrary() []GpuInfoList {
 	for _, info := range l {
 		found := false
 		requested := info.Library
-		if info.Variant != "" {
-			requested += "_" + info.Variant
+		if info.Variant != CPUCapabilityNone {
+			requested += "_" + info.Variant.String()
 		}
 		for i, lib := range libs {
 			if lib == requested {
@@ -86,3 +113,26 @@ type ByFreeMemory []GpuInfo
 func (a ByFreeMemory) Len() int           { return len(a) }
 func (a ByFreeMemory) Swap(i, j int)      { a[i], a[j] = a[j], a[i] }
 func (a ByFreeMemory) Less(i, j int) bool { return a[i].FreeMemory < a[j].FreeMemory }
+
+type CPUCapability uint32
+
+// Override at build time when building base GPU runners
+var GPURunnerCPUCapability = CPUCapabilityAVX
+
+const (
+	CPUCapabilityNone CPUCapability = iota
+	CPUCapabilityAVX
+	CPUCapabilityAVX2
+	// TODO AVX512
+)
+
+func (c CPUCapability) String() string {
+	switch c {
+	case CPUCapabilityAVX:
+		return "avx"
+	case CPUCapabilityAVX2:
+		return "avx2"
+	default:
+		return "no vector extensions"
+	}
+}

integration/concurrency_test.go

@@ -19,17 +19,19 @@ func TestMultiModelConcurrency(t *testing.T) {
 	var (
 		req = [2]api.GenerateRequest{
 			{
-				Model:  "orca-mini",
-				Prompt: "why is the ocean blue?",
-				Stream: &stream,
+				Model:     "orca-mini",
+				Prompt:    "why is the ocean blue?",
+				Stream:    &stream,
+				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:  "tinydolphin",
-				Prompt: "what is the origin of the us thanksgiving holiday?",
-				Stream: &stream,
+				Model:     "tinydolphin",
+				Prompt:    "what is the origin of the us thanksgiving holiday?",
+				Stream:    &stream,
+				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
@@ -38,42 +40,64 @@ func TestMultiModelConcurrency(t *testing.T) {
 		}
 		resp = [2][]string{
 			[]string{"sunlight"},
-			[]string{"england", "english", "massachusetts", "pilgrims"},
+			[]string{"england", "english", "massachusetts", "pilgrims", "british"},
 		}
 	)
 	var wg sync.WaitGroup
 	wg.Add(len(req))
-	ctx, cancel := context.WithTimeout(context.Background(), time.Second*120)
+	ctx, cancel := context.WithTimeout(context.Background(), time.Second*240)
 	defer cancel()
+
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+
+	for i := 0; i < len(req); i++ {
+		require.NoError(t, PullIfMissing(ctx, client, req[i].Model))
+	}
+
 	for i := 0; i < len(req); i++ {
 		go func(i int) {
 			defer wg.Done()
-			GenerateTestHelper(ctx, t, req[i], resp[i])
+			DoGenerate(ctx, t, client, req[i], resp[i], 60*time.Second, 10*time.Second)
 		}(i)
 	}
 	wg.Wait()
 }
 
 func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute) // GTX 750 2G card takes ~9 minutes
+	req, resp := GenerateRequests()
+	reqLimit := len(req)
+	iterLimit := 5
+
+	vram := os.Getenv("OLLAMA_MAX_VRAM")
+	if vram != "" {
+		max, err := strconv.ParseUint(vram, 10, 64)
+		require.NoError(t, err)
+		// Don't hammer on small VRAM cards...
+		if max < 4*1024*1024*1024 {
+			reqLimit = min(reqLimit, 2)
+			iterLimit = 2
+		}
+	}
+
+	ctx, cancel := context.WithTimeout(context.Background(), 9*time.Minute)
 	defer cancel()
 	client, _, cleanup := InitServerConnection(ctx, t)
 	defer cleanup()
 
-	req, resp := GenerateRequests()
 	// Get the server running (if applicable) warm the model up with a single initial request
-	DoGenerate(ctx, t, client, req[0], resp[0], 60*time.Second, 5*time.Second)
+	DoGenerate(ctx, t, client, req[0], resp[0], 60*time.Second, 10*time.Second)
 
 	var wg sync.WaitGroup
-	wg.Add(len(req))
-	for i := 0; i < len(req); i++ {
+	wg.Add(reqLimit)
+	for i := 0; i < reqLimit; i++ {
 		go func(i int) {
 			defer wg.Done()
-			for j := 0; j < 5; j++ {
+			for j := 0; j < iterLimit; j++ {
 				slog.Info("Starting", "req", i, "iter", j)
-				// On slower GPUs it can take a while to process the 4 concurrent requests
+				// On slower GPUs it can take a while to process the concurrent requests
 				// so we allow a much longer initial timeout
-				DoGenerate(ctx, t, client, req[i], resp[i], 90*time.Second, 5*time.Second)
+				DoGenerate(ctx, t, client, req[i], resp[i], 120*time.Second, 20*time.Second)
 			}
 		}(i)
 	}
@@ -221,5 +245,23 @@ func TestMultiModelStress(t *testing.T) {
 			}
 		}(i)
 	}
+	go func() {
+		for {
+			time.Sleep(2 * time.Second)
+			select {
+			case <-ctx.Done():
+				return
+			default:
+				models, err := client.ListRunning(ctx)
+				if err != nil {
+					slog.Warn("failed to list running models", "error", err)
+					continue
+				}
+				for _, m := range models.Models {
+					slog.Info("loaded model snapshot", "model", m)
+				}
+			}
+		}
+	}()
 	wg.Wait()
 }

integration/context_test.go

@@ -11,7 +11,8 @@ import (
 )
 
 func TestContextExhaustion(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute) // TODO maybe shorter?
+	// Longer needed for small footprint GPUs
+	ctx, cancel := context.WithTimeout(context.Background(), 6*time.Minute)
 	defer cancel()
 	// Set up the test data
 	req := api.GenerateRequest{

integration/llm_image_test.go

@@ -32,7 +32,11 @@ func TestIntegrationMultimodal(t *testing.T) {
 	resp := "the ollam"
 	ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
 	defer cancel()
-	GenerateTestHelper(ctx, t, req, []string{resp})
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+	require.NoError(t, PullIfMissing(ctx, client, req.Model))
+	// llava models on CPU can be quite slow to start,
+	DoGenerate(ctx, t, client, req, []string{resp}, 120*time.Second, 30*time.Second)
 }
 
 const imageEncoding = `iVBORw0KGgoAAAANSUhEUgAAANIAAAB4CAYAAACHHqzKAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEb

integration/utils_test.go

@@ -140,7 +140,7 @@ func PullIfMissing(ctx context.Context, client *api.Client, modelName string) er
 
 	showCtx, cancel := context.WithDeadlineCause(
 		ctx,
-		time.Now().Add(5*time.Second),
+		time.Now().Add(10*time.Second),
 		fmt.Errorf("show for existing model %s took too long", modelName),
 	)
 	defer cancel()
@@ -287,41 +287,46 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 func GenerateRequests() ([]api.GenerateRequest, [][]string) {
 	return []api.GenerateRequest{
 			{
-				Model:  "orca-mini",
-				Prompt: "why is the ocean blue?",
-				Stream: &stream,
+				Model:     "orca-mini",
+				Prompt:    "why is the ocean blue?",
+				Stream:    &stream,
+				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:  "orca-mini",
-				Prompt: "why is the color of dirt brown?",
-				Stream: &stream,
+				Model:     "orca-mini",
+				Prompt:    "why is the color of dirt brown?",
+				Stream:    &stream,
+				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:  "orca-mini",
-				Prompt: "what is the origin of the us thanksgiving holiday?",
-				Stream: &stream,
+				Model:     "orca-mini",
+				Prompt:    "what is the origin of the us thanksgiving holiday?",
+				Stream:    &stream,
+				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:  "orca-mini",
-				Prompt: "what is the origin of independence day?",
-				Stream: &stream,
+				Model:     "orca-mini",
+				Prompt:    "what is the origin of independence day?",
+				Stream:    &stream,
+				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:  "orca-mini",
-				Prompt: "what is the composition of air?",
-				Stream: &stream,
+				Model:     "orca-mini",
+				Prompt:    "what is the composition of air?",
+				Stream:    &stream,
+				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
@@ -331,7 +336,7 @@ func GenerateRequests() ([]api.GenerateRequest, [][]string) {
 		[][]string{
 			[]string{"sunlight"},
 			[]string{"soil", "organic", "earth", "black", "tan"},
-			[]string{"england", "english", "massachusetts", "pilgrims"},
+			[]string{"england", "english", "massachusetts", "pilgrims", "british"},
 			[]string{"fourth", "july", "declaration", "independence"},
 			[]string{"nitrogen", "oxygen", "carbon", "dioxide"},
 		}

llm/ext_server/server.cpp

@@ -56,7 +56,6 @@ struct server_params {
     std::string hostname = "127.0.0.1";
     std::vector<std::string> api_keys;
     std::string public_path = "examples/server/public";
-    std::string chat_template = "";
     int32_t port = 8080;
     int32_t read_timeout = 600;
     int32_t write_timeout = 600;
@@ -427,16 +426,6 @@ struct llama_server_context
         return true;
     }
 
-    void validate_model_chat_template(server_params & sparams) {
-        llama_chat_message chat[] = {{"user", "test"}};
-        std::vector<char> buf(1);
-        int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size());
-        if (res < 0) {
-            LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
-            sparams.chat_template = "chatml";
-        }
-    }
-
     void initialize() {
         // create slots
         all_slots_are_idle = true;
@@ -2335,9 +2324,9 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
                 invalid_param = true;
                 break;
             }
-#ifndef GGML_USE_CUBLAS
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n");
-#endif // GGML_USE_CUBLAS
+#ifndef GGML_USE_CUDA
+            fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n");
+#endif // GGML_USE_CUDA
         }
         else if (arg == "--tensor-split" || arg == "-ts")
         {
@@ -2346,7 +2335,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
                 invalid_param = true;
                 break;
             }
-#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
             std::string arg_next = argv[i];
 
             // split string by , and /
@@ -2367,8 +2356,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
                 }
             }
 #else
-            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
-#endif // GGML_USE_CUBLAS
+            LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {});
+#endif // GGML_USE_CUDA
         }
         else if (arg == "--main-gpu" || arg == "-mg")
         {
@@ -2377,7 +2366,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
                 invalid_param = true;
                 break;
             }
-#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
             params.main_gpu = std::stoi(argv[i]);
 #else
             LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
@@ -2535,7 +2524,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
                 invalid_param = true;
                 break;
             }
-            sparams.chat_template = argv[i];
         }
         else if (arg == "--override-kv")
         {
@@ -3008,11 +2996,6 @@ int main(int argc, char **argv) {
     }
     const auto model_meta = llama.model_meta();
 
-    if (sparams.chat_template.empty()) { // custom chat template is not supplied
-        // check if the template comes with the model is supported by us
-        llama.validate_model_chat_template(sparams);
-    }
-
     // Middleware for API key validation
     auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
         // If API key is not set, skip validation

llm/generate/gen_darwin.sh

@@ -18,7 +18,7 @@ sign() {
     fi
 }
 
-COMMON_DARWIN_DEFS="-DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DLLAMA_METAL_MACOSX_VERSION_MIN=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DLLAMA_METAL_EMBED_LIBRARY=on"
+COMMON_DARWIN_DEFS="-DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DLLAMA_METAL_MACOSX_VERSION_MIN=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DLLAMA_METAL_EMBED_LIBRARY=on -DLLAMA_OPENMP=off"
 
 case "${GOARCH}" in
 "amd64")
@@ -27,7 +27,7 @@ case "${GOARCH}" in
     # Static build for linking into the Go binary
     init_vars
     CMAKE_TARGETS="--target llama --target ggml"
-    CMAKE_DEFS="${COMMON_CPU_DEFS} -DBUILD_SHARED_LIBS=off -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
+    CMAKE_DEFS="${COMMON_CPU_DEFS} -DBUILD_SHARED_LIBS=off -DLLAMA_BLAS=off -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
     BUILD_DIR="../build/darwin/${ARCH}_static"
     echo "Building static library"
     build
@@ -37,7 +37,7 @@ case "${GOARCH}" in
         # CPU first for the default library, set up as lowest common denominator for maximum compatibility (including Rosetta)
         #
         init_vars
-        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
+        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=off -DLLAMA_BLAS=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
         BUILD_DIR="../build/darwin/${ARCH}/cpu"
         echo "Building LCD CPU"
         build
@@ -49,7 +49,7 @@ case "${GOARCH}" in
         # Approximately 400% faster than LCD on same CPU
         #
         init_vars
-        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
+        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=off -DLLAMA_BLAS=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
         BUILD_DIR="../build/darwin/${ARCH}/cpu_avx"
         echo "Building AVX CPU"
         build
@@ -61,7 +61,7 @@ case "${GOARCH}" in
         # Approximately 10% faster than AVX on same CPU
         #
         init_vars
-        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=on -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on ${CMAKE_DEFS}"
+        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=on -DLLAMA_BLAS=off -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on ${CMAKE_DEFS}"
         BUILD_DIR="../build/darwin/${ARCH}/cpu_avx2"
         echo "Building AVX2 CPU"
         EXTRA_LIBS="${EXTRA_LIBS} -framework Accelerate -framework Foundation"
@@ -75,7 +75,7 @@ case "${GOARCH}" in
     # Static build for linking into the Go binary
     init_vars
     CMAKE_TARGETS="--target llama --target ggml"
-    CMAKE_DEFS="-DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DBUILD_SHARED_LIBS=off -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} -DLLAMA_METAL=off -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
+    CMAKE_DEFS="-DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DLLAMA_BLAS=off -DCMAKE_SYSTEM_NAME=Darwin -DBUILD_SHARED_LIBS=off -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} -DLLAMA_METAL=off -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
     BUILD_DIR="../build/darwin/${ARCH}_static"
     echo "Building static library"
     build

llm/generate/gen_linux.sh

@@ -51,7 +51,7 @@ if [ -z "${CUDACXX}" ]; then
         export CUDACXX=$(command -v nvcc)
     fi
 fi
-COMMON_CMAKE_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off"
+COMMON_CMAKE_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off -DLLAMA_OPENMP=off"
 source $(dirname $0)/gen_common.sh
 init_vars
 git_module_setup
@@ -64,7 +64,7 @@ if [ -z "${OLLAMA_SKIP_STATIC_GENERATE}" -o "${OLLAMA_CPU_TARGET}" = "static" ];
     # Static build for linking into the Go binary
     init_vars
     CMAKE_TARGETS="--target llama --target ggml"
-    CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DLLAMA_NATIVE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
+    CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DLLAMA_NATIVE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off -DLLAMA_OPENMP=off ${CMAKE_DEFS}"
     BUILD_DIR="../build/linux/${ARCH}_static"
     echo "Building static library"
     build
@@ -93,7 +93,7 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
         # -DLLAMA_AVX512_VBMI -- 2018 Intel Cannon Lake
         # -DLLAMA_AVX512_VNNI -- 2021 Intel Alder Lake
 
-        COMMON_CPU_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off"
+        COMMON_CPU_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off -DLLAMA_OPENMP=off"
         if [ -z "${OLLAMA_CPU_TARGET}" -o "${OLLAMA_CPU_TARGET}" = "cpu" ]; then
             #
             # CPU first for the default library, set up as lowest common denominator for maximum compatibility (including Rosetta)
@@ -178,7 +178,7 @@ if [ -z "${OLLAMA_SKIP_CUDA_GENERATE}" -a -d "${CUDA_LIB_DIR}" ]; then
         CMAKE_CUDA_DEFS="-DLLAMA_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${OLLAMA_CUSTOM_CUDA_DEFS}"
         echo "Building custom CUDA GPU"
     else
-        CMAKE_CUDA_DEFS="-DLLAMA_CUDA=on -DLLAMA_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}"
+        CMAKE_CUDA_DEFS="-DLLAMA_CUDA=on -DCMAKE_CUDA_FLAGS=-t8 -DLLAMA_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}"
     fi
     CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} ${ARM64_DEFS} ${CMAKE_CUDA_DEFS}"
     BUILD_DIR="../build/linux/${ARCH}/cuda${CUDA_VARIANT}"

llm/generate/gen_windows.ps1

@@ -39,7 +39,8 @@ function init_vars {
     }
     $script:cmakeDefs = @(
         "-DBUILD_SHARED_LIBS=on",
-        "-DLLAMA_NATIVE=off"
+        "-DLLAMA_NATIVE=off",
+        "-DLLAMA_OPENMP=off"
         )
     $script:commonCpuDefs = @("-DCMAKE_POSITION_INDEPENDENT_CODE=on")
     $script:ARCH = $Env:PROCESSOR_ARCHITECTURE.ToLower()
@@ -122,8 +123,13 @@ function build {
     & cmake --version
     & cmake -S "${script:llamacppDir}" -B $script:buildDir $script:cmakeDefs
     if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
-    write-host "building with: cmake --build $script:buildDir --config $script:config $($script:cmakeTargets | ForEach-Object { `"--target`", $_ })"
-    & cmake --build $script:buildDir --config $script:config ($script:cmakeTargets | ForEach-Object { "--target", $_ })
+    if ($cmakeDefs -contains "-G") {
+        $extra=@("-j8")
+    } else {
+        $extra= @("--", "/p:CL_MPcount=8")
+    }
+    write-host "building with: cmake --build $script:buildDir --config $script:config $($script:cmakeTargets | ForEach-Object { `"--target`", $_ }) $extra"
+    & cmake --build $script:buildDir --config $script:config ($script:cmakeTargets | ForEach-Object { "--target", $_ }) $extra
     if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
     # Rearrange output to be consistent between different generators
     if ($null -ne ${script:config} -And (test-path -path "${script:buildDir}/bin/${script:config}" ) ) {
@@ -203,7 +209,8 @@ function build_static() {
             "-DLLAMA_AVX2=off",
             "-DLLAMA_AVX512=off",
             "-DLLAMA_F16C=off",
-            "-DLLAMA_FMA=off")
+            "-DLLAMA_FMA=off",
+            "-DLLAMA_OPENMP=off")
         $script:buildDir="../build/windows/${script:ARCH}_static"
         write-host "Building static library"
         build
@@ -270,7 +277,15 @@ function build_cuda() {
         init_vars
         $script:buildDir="../build/windows/${script:ARCH}/cuda$script:CUDA_VARIANT"
         $script:distDir="$script:DIST_BASE\cuda$script:CUDA_VARIANT"
-        $script:cmakeDefs += @("-A", "x64", "-DLLAMA_CUDA=ON", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=off", "-DCUDAToolkit_INCLUDE_DIR=$script:CUDA_INCLUDE_DIR", "-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}")
+        $script:cmakeDefs += @(
+            "-A", "x64",
+            "-DLLAMA_CUDA=ON",
+            "-DLLAMA_AVX=on",
+            "-DLLAMA_AVX2=off",
+            "-DCUDAToolkit_INCLUDE_DIR=$script:CUDA_INCLUDE_DIR",
+            "-DCMAKE_CUDA_FLAGS=-t8",
+            "-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}"
+            )
         if ($null -ne $env:OLLAMA_CUSTOM_CUDA_DEFS) {
             write-host "OLLAMA_CUSTOM_CUDA_DEFS=`"${env:OLLAMA_CUSTOM_CUDA_DEFS}`""
             $script:cmakeDefs +=@("${env:OLLAMA_CUSTOM_CUDA_DEFS}")
@@ -280,10 +295,12 @@ function build_cuda() {
         sign
         install
 
-        write-host "copying CUDA dependencies to ${script:SRC_DIR}\dist\windows-${script:ARCH}\"
-        cp "${script:CUDA_LIB_DIR}\cudart64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
-        cp "${script:CUDA_LIB_DIR}\cublas64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
-        cp "${script:CUDA_LIB_DIR}\cublasLt64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
+        rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        md "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\" -ea 0 > $null
+        write-host "copying CUDA dependencies to ${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        cp "${script:CUDA_LIB_DIR}\cudart64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        cp "${script:CUDA_LIB_DIR}\cublas64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        cp "${script:CUDA_LIB_DIR}\cublasLt64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
     } else {
         write-host "Skipping CUDA generation step"
     }
@@ -317,16 +334,18 @@ function build_oneapi() {
     sign
     install
 
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libirngmd.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libmmd.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_level_zero.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_unified_runtime.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_win_proxy_loader.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\svml_dispmd.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\sycl7.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_core.2.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_sycl_blas.4.dll" "${script:distDir}"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_tbb_thread.2.dll" "${script:distDir}"
+    rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    md "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\" -ea 0 > $null
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libirngmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libmmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_level_zero.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_unified_runtime.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_win_proxy_loader.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\svml_dispmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\sycl7.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_core.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_sycl_blas.4.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_tbb_thread.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
   } else {
     Write-Host "Skipping oneAPI generation step"
   }

llm/ggla.go

@@ -53,7 +53,7 @@ func (llm *ggla) Tensors() Tensors {
 	return llm.tensors
 }
 
-func (llm *ggla) decode(rs io.ReadSeeker) error {
+func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
 	var r uint32
 	if err := binary.Read(rs, binary.LittleEndian, &r); err != nil {
 		return err
@@ -69,9 +69,18 @@ func (llm *ggla) decode(rs io.ReadSeeker) error {
 	for {
 		var dims uint32
 		if err := binary.Read(rs, binary.LittleEndian, &dims); err != nil {
+			if errors.Is(err, io.EOF) {
+				return nil
+			}
 			return err
 		}
 
+		defer func() {
+			if errors.Is(retErr, io.EOF) {
+				retErr = io.ErrUnexpectedEOF
+			}
+		}()
+
 		var namesize uint32
 		if err := binary.Read(rs, binary.LittleEndian, &namesize); err != nil {
 			return err
@@ -108,7 +117,7 @@ func (llm *ggla) decode(rs io.ReadSeeker) error {
 			return err
 		}
 
-		if _, err := rs.Seek((offset+31)&-32, io.SeekStart); err != nil {
+		if _, err := rs.Seek((offset+31)&-32-offset, io.SeekCurrent); err != nil {
 			return err
 		}
 

llm/ggml.go

@@ -6,6 +6,8 @@ import (
 	"fmt"
 	"io"
 	"strings"
+
+	"github.com/ollama/ollama/util/bufioutil"
 )
 
 type GGML struct {
@@ -69,6 +71,30 @@ func (kv KV) HeadCountKV() uint64 {
 	return 1
 }
 
+func (kv KV) EmbeddingHeadCount() uint64 {
+	if heads := kv.HeadCount(); heads > 0 {
+		return kv.EmbeddingLength() / kv.HeadCount()
+	}
+
+	return 0
+}
+
+func (kv KV) EmbeddingHeadCountK() uint64 {
+	if k := kv.u64(fmt.Sprintf("%s.attention.key_length", kv.Architecture())); k > 0 {
+		return k
+	}
+
+	return kv.EmbeddingHeadCount()
+}
+
+func (kv KV) EmbeddingHeadCountV() uint64 {
+	if v := kv.u64(fmt.Sprintf("%s.attention.value_length", kv.Architecture())); v > 0 {
+		return v
+	}
+
+	return kv.EmbeddingHeadCount()
+}
+
 func (kv KV) GQA() uint64 {
 	return kv.HeadCount() / kv.HeadCountKV()
 }
@@ -254,7 +280,18 @@ func DetectGGMLType(b []byte) string {
 	}
 }
 
-func DecodeGGML(rs io.ReadSeeker) (*GGML, int64, error) {
+// DecodeGGML decodes a GGML model from the given reader.
+//
+// It collects array values for arrays with a size less than or equal to
+// maxArraySize. If maxArraySize is 0, the default value of 1024 is used. If
+// the maxArraySize is negative, all arrays are collected.
+func DecodeGGML(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
+	if maxArraySize == 0 {
+		maxArraySize = 1024
+	}
+
+	rs = bufioutil.NewBufferedSeeker(rs, 32<<10)
+
 	var magic uint32
 	if err := binary.Read(rs, binary.LittleEndian, &magic); err != nil {
 		return nil, 0, err
@@ -267,17 +304,15 @@ func DecodeGGML(rs io.ReadSeeker) (*GGML, int64, error) {
 	case FILE_MAGIC_GGLA:
 		c = &containerGGLA{}
 	case FILE_MAGIC_GGUF_LE:
-		c = &containerGGUF{ByteOrder: binary.LittleEndian}
+		c = &containerGGUF{ByteOrder: binary.LittleEndian, maxArraySize: maxArraySize}
 	case FILE_MAGIC_GGUF_BE:
-		c = &containerGGUF{ByteOrder: binary.BigEndian}
+		c = &containerGGUF{ByteOrder: binary.BigEndian, maxArraySize: maxArraySize}
 	default:
 		return nil, 0, errors.New("invalid file magic")
 	}
 
 	model, err := c.Decode(rs)
-	if errors.Is(err, io.EOF) {
-		// noop
-	} else if err != nil {
+	if err != nil {
 		return nil, 0, err
 	}
 
@@ -297,7 +332,10 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 	embedding := llm.KV().EmbeddingLength()
 	heads := llm.KV().HeadCount()
 	headsKV := llm.KV().HeadCountKV()
-	vocab := uint64(len(llm.KV()["tokenizer.ggml.tokens"].([]any)))
+	vocab := uint64(llm.KV()["tokenizer.ggml.tokens"].(*array).size)
+
+	embeddingHeads := llm.KV().EmbeddingHeadCount()
+	embeddingHeadsK := llm.KV().EmbeddingHeadCountK()
 
 	layers := llm.Tensors().Layers()
 
@@ -307,7 +345,8 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 
 		partialOffload = 4 * batch * embedding
 		partialOffload += max(
-			4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embedding/heads*headsKV),
+			// 4*batch*(4+6*embedding+context*(2*heads)+llm.KV().GQA()),
+			4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embeddingHeads*headsKV),
 			4*batch*(embedding+vocab)+embedding*vocab*105/128,
 		)
 
@@ -315,15 +354,15 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 			// mixtral 8x22b
 			ff := uint64(llm.KV()["llama.feed_forward_length"].(uint32))
 			partialOffload = max(
-				3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embedding/heads*headsKV),
-				4*(context*batch*heads+context*embedding/heads*headsKV+batch*1024+embedding/heads*headsKV*batch),
+				3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embeddingHeads*headsKV),
+				4*(context*batch*heads+context*embeddingHeads*headsKV+batch*1024+embeddingHeads*headsKV*batch),
 			)
 		} else if ffnGateWeight, ok := layers["blk.0"]["ffn_gate.0.weight"]; ok {
 			// mixtral 8x7b
 			ffnGateWeight1 := ffnGateWeight.Shape[1]
 			fullOffload = 4 * batch * (2 + 3*embedding + context*(1+heads) + 2*headsKV + ffnGateWeight1)
 			partialOffload = max(
-				4*batch*(3+embedding/heads*headsKV+embedding+context*(1+heads)+ffnGateWeight1)+(embedding*embedding+3*embedding*headsKV*ffnGateWeight1)*9/16,
+				4*batch*(3+embeddingHeads*headsKV+embedding+context*(1+heads)+ffnGateWeight1)+(embedding*embedding+3*embedding*headsKV*ffnGateWeight1)*9/16,
 				4*batch*(1+2*embedding+context*(1+heads))+embedding*(6*context*headsKV/heads+embedding*9/16),
 			)
 		}
@@ -366,6 +405,16 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 			4*batch*(vocab+2*embedding),
 			fullOffload,
 		)
+	case "deepseek2":
+		fullOffload = max(
+			4*batch*(3*embedding+vocab),
+			4*batch*(3*embedding+2+context*(1+headsKV)+2*embeddingHeadsK*headsKV),
+		)
+
+		partialOffload = max(
+			4*batch*(3*embedding+vocab)+embedding*vocab*105/128,
+			4*batch*(2*embedding+1+2*embeddingHeadsK*headsKV+context+context*headsKV)+4*embeddingHeadsK*context*headsKV+embedding*embeddingHeadsK*headsKV*9/16,
+		)
 	}
 
 	return

llm/ggml_test.go

@@ -0,0 +1 @@
+package llm

llm/gguf.go

@@ -3,11 +3,10 @@ package llm
 import (
 	"bytes"
 	"encoding/binary"
+	"encoding/json"
 	"fmt"
 	"io"
 	"strings"
-
-	"log/slog"
 )
 
 type containerGGUF struct {
@@ -29,6 +28,12 @@ type containerGGUF struct {
 		NumTensor uint64
 		NumKV     uint64
 	}
+
+	maxArraySize int
+}
+
+func (c *containerGGUF) canCollectArray(size int) bool {
+	return c.maxArraySize < 0 || size <= c.maxArraySize
 }
 
 func (c *containerGGUF) Name() string {
@@ -54,7 +59,6 @@ func (c *containerGGUF) Decode(rs io.ReadSeeker) (model, error) {
 	}
 
 	model := newGGUF(c)
-	slog.Debug(fmt.Sprintf("model = %#v", model))
 	if err := model.Decode(rs); err != nil {
 		return nil, err
 	}
@@ -85,6 +89,8 @@ type gguf struct {
 	tensors []*Tensor
 
 	parameters uint64
+
+	scratch [16 << 10]byte
 }
 
 func newGGUF(container *containerGGUF) *gguf {
@@ -181,34 +187,34 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 	}
 
 	// decode tensors
-	for i := 0; uint64(i) < llm.numTensor(); i++ {
+	for range llm.numTensor() {
 		name, err := readGGUFString(llm, rs)
 		if err != nil {
-			return err
+			return fmt.Errorf("failed to read tensor name: %w", err)
 		}
 
 		// dims is the number of dimensions in the tensor
 		dims, err := readGGUF[uint32](llm, rs)
 		if err != nil {
-			return err
+			return fmt.Errorf("failed to read tensor dimensions: %w", err)
 		}
 
 		shape := [4]uint64{1, 1, 1, 1}
 		for i := 0; uint32(i) < dims; i++ {
 			shape[i], err = readGGUF[uint64](llm, rs)
 			if err != nil {
-				return err
+				return fmt.Errorf("failed to read tensor shape: %w", err)
 			}
 		}
 
 		kind, err := readGGUF[uint32](llm, rs)
 		if err != nil {
-			return err
+			return fmt.Errorf("failed to read tensor kind: %w", err)
 		}
 
 		offset, err := readGGUF[uint64](llm, rs)
 		if err != nil {
-			return err
+			return fmt.Errorf("failed to read tensor offset: %w", err)
 		}
 
 		tensor := Tensor{
@@ -230,24 +236,19 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		alignment = 32
 	}
 
-	offset, err := rs.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-
-	padding := llm.padding(offset, int64(alignment))
-	if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
-		return err
-	}
-
 	for _, tensor := range llm.tensors {
-		if _, err := rs.Seek(int64(tensor.Size()), io.SeekCurrent); err != nil {
-			return err
+		offset, err := rs.Seek(0, io.SeekCurrent)
+		if err != nil {
+			return fmt.Errorf("failed to get current offset: %w", err)
 		}
 
-		padding := llm.padding(int64(tensor.Size()), int64(alignment))
+		padding := llm.padding(offset, int64(alignment))
 		if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
-			return err
+			return fmt.Errorf("failed to seek to init padding: %w", err)
+		}
+
+		if _, err := rs.Seek(int64(tensor.Size()), io.SeekCurrent); err != nil {
+			return fmt.Errorf("failed to seek to tensor: %w", err)
 		}
 	}
 
@@ -285,22 +286,48 @@ func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
 	return b.String(), nil
 }
 
+func discardGGUFString(llm *gguf, r io.Reader) error {
+	buf := llm.scratch[:8]
+	_, err := io.ReadFull(r, buf)
+	if err != nil {
+		return err
+	}
+
+	size := int(llm.ByteOrder.Uint64(buf))
+	for size > 0 {
+		n, err := r.Read(llm.scratch[:min(size, cap(llm.scratch))])
+		if err != nil {
+			return err
+		}
+		size -= n
+	}
+	return nil
+}
+
 func readGGUFString(llm *gguf, r io.Reader) (string, error) {
 	if llm.Version == 1 {
 		return readGGUFV1String(llm, r)
 	}
 
-	var length uint64
-	if err := binary.Read(r, llm.ByteOrder, &length); err != nil {
+	buf := llm.scratch[:8]
+	_, err := io.ReadFull(r, buf)
+	if err != nil {
 		return "", err
 	}
 
-	var b bytes.Buffer
-	if _, err := io.CopyN(&b, r, int64(length)); err != nil {
+	length := int(llm.ByteOrder.Uint64(buf))
+	if length > len(llm.scratch) {
+		buf = make([]byte, length)
+	} else {
+		buf = llm.scratch[:length]
+	}
+	clear(buf)
+
+	_, err = io.ReadFull(r, buf)
+	if err != nil {
 		return "", err
 	}
-
-	return b.String(), nil
+	return string(buf), nil
 }
 
 func writeGGUFString(llm *gguf, w io.Writer, s string) error {
@@ -316,7 +343,16 @@ func writeGGUFString(llm *gguf, w io.Writer, s string) error {
 	return err
 }
 
-func readGGUFV1Array(llm *gguf, r io.Reader) (a []any, err error) {
+type array struct {
+	size   int
+	values []any
+}
+
+func (a *array) MarshalJSON() ([]byte, error) {
+	return json.Marshal(a.values)
+}
+
+func readGGUFV1Array(llm *gguf, r io.Reader) (*array, error) {
 	t, err := readGGUF[uint32](llm, r)
 	if err != nil {
 		return nil, err
@@ -327,7 +363,12 @@ func readGGUFV1Array(llm *gguf, r io.Reader) (a []any, err error) {
 		return nil, err
 	}
 
-	for i := 0; uint32(i) < n; i++ {
+	a := &array{size: int(n)}
+	if llm.canCollectArray(int(n)) {
+		a.values = make([]any, 0, int(n))
+	}
+
+	for i := range n {
 		var e any
 		switch t {
 		case ggufTypeUint8:
@@ -361,13 +402,15 @@ func readGGUFV1Array(llm *gguf, r io.Reader) (a []any, err error) {
 			return nil, err
 		}
 
-		a = append(a, e)
+		if a.values != nil {
+			a.values[i] = e
+		}
 	}
 
-	return
+	return a, nil
 }
 
-func readGGUFArray(llm *gguf, r io.Reader) (a []any, err error) {
+func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 	if llm.Version == 1 {
 		return readGGUFV1Array(llm, r)
 	}
@@ -382,7 +425,12 @@ func readGGUFArray(llm *gguf, r io.Reader) (a []any, err error) {
 		return nil, err
 	}
 
-	for i := 0; uint64(i) < n; i++ {
+	a := &array{size: int(n)}
+	if llm.canCollectArray(int(n)) {
+		a.values = make([]any, int(n))
+	}
+
+	for i := range n {
 		var e any
 		switch t {
 		case ggufTypeUint8:
@@ -408,7 +456,11 @@ func readGGUFArray(llm *gguf, r io.Reader) (a []any, err error) {
 		case ggufTypeBool:
 			e, err = readGGUF[bool](llm, r)
 		case ggufTypeString:
-			e, err = readGGUFString(llm, r)
+			if a.values != nil {
+				e, err = readGGUFString(llm, r)
+			} else {
+				err = discardGGUFString(llm, r)
+			}
 		default:
 			return nil, fmt.Errorf("invalid array type: %d", t)
 		}
@@ -416,10 +468,12 @@ func readGGUFArray(llm *gguf, r io.Reader) (a []any, err error) {
 			return nil, err
 		}
 
-		a = append(a, e)
+		if a.values != nil {
+			a.values[i] = e
+		}
 	}
 
-	return
+	return a, nil
 }
 
 func writeGGUFArray[S ~[]E, E any](llm *gguf, w io.Writer, t uint32, s S) error {

llm/memory.go

@@ -3,9 +3,10 @@ package llm
 import (
 	"fmt"
 	"log/slog"
+	"strconv"
+	"strings"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/gpu"
 )
@@ -16,7 +17,8 @@ func PredictServerFit(allGpus gpu.GpuInfoList, ggml *GGML, adapters, projectors
 	var estimatedVRAM uint64
 	for _, gpus := range allGpus.ByLibrary() {
 		var layerCount int
-		layerCount, estimatedVRAM, _ = EstimateGPULayers(gpus, ggml, projectors, opts)
+		estimate := EstimateGPULayers(gpus, ggml, projectors, opts)
+		layerCount, estimatedVRAM = estimate.Layers, estimate.VRAMSize
 		if opts.NumGPU < 0 {
 			if layerCount > 0 && layerCount >= int(ggml.KV().BlockCount()+1) {
 				return true, estimatedVRAM
@@ -30,24 +32,76 @@ func PredictServerFit(allGpus gpu.GpuInfoList, ggml *GGML, adapters, projectors
 	return false, estimatedVRAM
 }
 
+type MemoryEstimate struct {
+	// How many layers we predict we can load
+	Layers int
+
+	// The size of the graph which occupies the main GPU
+	Graph uint64
+
+	// How much VRAM will be allocated given the number of layers we predict
+	VRAMSize uint64
+
+	// The total size of the model if loaded into VRAM.  If all layers are loaded, VRAMSize == TotalSize
+	TotalSize uint64
+
+	// For multi-GPU scenarios, this provides the tensor split parameter
+	TensorSplit string
+
+	// For multi-GPU scenarios, this is the size in bytes per GPU
+	GPUSizes []uint64
+
+	// internal fields for logging purposes
+	inferenceLibrary    string
+	layersRequested     int
+	layersModel         int
+	availableList       []string
+	kv                  uint64
+	allocationsList     []string
+	memoryWeights       uint64
+	memoryLayerOutput   uint64
+	graphFullOffload    uint64
+	graphPartialOffload uint64
+}
+
 // Given a model and one or more GPU targets, predict how many layers and bytes we can load, and the total size
 // The GPUs provided must all be the same Library
-func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts api.Options) (int, uint64, uint64) {
-	var memoryAvailable uint64
-	for _, info := range gpus {
-		memoryAvailable += info.FreeMemory
-	}
-	if envconfig.MaxVRAM > 0 {
-		memoryAvailable = envconfig.MaxVRAM
-	}
+func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts api.Options) MemoryEstimate {
+	// Graph size for a partial offload, applies to all GPUs
+	var graphPartialOffload uint64
 
-	slog.Debug("evaluating", "library", gpus[0].Library, "gpu_count", len(gpus), "available", format.HumanBytes2(memoryAvailable))
+	// Graph size when all layers are offloaded, applies to all GPUs
+	var graphFullOffload uint64
 
-	// TODO - this is probably wrong, first GPU vs secondaries will have different overheads
-	memoryMinimum := gpus[0].MinimumMemory
+	// Final graph offload once we know full or partial
+	var graphOffload uint64
+
+	// Projectors loaded into GPU0 only
+	var projectorSize uint64
+
+	// Conditional output size on GPU 0
+	var memoryLayerOutput uint64
+
+	// The sizes of a layer
+	var layerSize uint64
+
+	// The sum of all the layer sizes (just for logging)
+	var memoryWeights uint64
+
+	// True if all the layers are loaded
+	var fullyLoaded bool
+
+	// Overflow that didn't fit into the GPU
+	var overflow uint64
+
+	availableList := make([]string, len(gpus))
+	for i, gpu := range gpus {
+		availableList[i] = format.HumanBytes2(gpu.FreeMemory)
+	}
+	slog.Debug("evaluating", "library", gpus[0].Library, "gpu_count", len(gpus), "available", availableList)
 
 	for _, projector := range projectors {
-		memoryMinimum += projectorMemoryRequirements(projector)
+		projectorSize += projectorMemoryRequirements(projector)
 
 		// multimodal models require at least 2048 context
 		opts.NumCtx = max(opts.NumCtx, 2048)
@@ -56,127 +110,246 @@ func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts
 	layers := ggml.Tensors().Layers()
 	// add one layer worth of memory as a buffer
 	if blk0, ok := layers["blk.0"]; ok {
-		memoryMinimum += blk0.size()
+		layerSize = blk0.size()
+	} else {
+		slog.Warn("model missing blk.0 layer size")
 	}
 
-	// fp16 k,v = (1 (k) + 1 (v)) * sizeof(float16) * n_ctx * n_layer * n_embd / n_head * n_head_kv
-	var kv uint64 = 2 * 2 * uint64(opts.NumCtx) * ggml.KV().BlockCount() * ggml.KV().EmbeddingLength() / ggml.KV().HeadCount() * ggml.KV().HeadCountKV()
+	// fp16 k,v = sizeof(float16) * n_ctx * n_layer * (n_embd_head_k + n_embd_head_v) * n_head_kv
+	var kv uint64 = 2 * uint64(opts.NumCtx) * ggml.KV().BlockCount() * (ggml.KV().EmbeddingHeadCountK() + ggml.KV().EmbeddingHeadCountV()) * ggml.KV().HeadCountKV()
 
-	graphPartialOffload, graphFullOffload := ggml.GraphSize(uint64(opts.NumCtx), uint64(min(opts.NumCtx, opts.NumBatch)))
+	// KV is proportional to the number of layers
+	layerSize += kv / ggml.KV().BlockCount()
+
+	graphPartialOffload, graphFullOffload = ggml.GraphSize(uint64(opts.NumCtx), uint64(min(opts.NumCtx, opts.NumBatch)))
 	if graphPartialOffload == 0 {
 		graphPartialOffload = ggml.KV().GQA() * kv / 6
 	}
-
 	if graphFullOffload == 0 {
 		graphFullOffload = graphPartialOffload
 	}
 
-	graphFullOffload *= uint64(len(gpus))
-	graphPartialOffload *= uint64(len(gpus))
-
 	// on metal there's no partial offload overhead
 	if gpus[0].Library == "metal" {
 		graphPartialOffload = graphFullOffload
+	} else if len(gpus) > 1 {
+		// multigpu should always use the partial graph size
+		graphFullOffload = graphPartialOffload
 	}
 
-	// memoryRequiredTotal represents the memory required for full GPU offloading (all layers)
-	memoryRequiredTotal := memoryMinimum + graphFullOffload
-
-	// memoryRequiredPartial represents the memory required for partial GPU offloading (n > 0, n < layers)
-	memoryRequiredPartial := memoryMinimum + graphPartialOffload
-
-	var memoryLayerOutput uint64
 	if layer, ok := layers["output_norm"]; ok {
 		memoryLayerOutput += layer.size()
 	}
-
 	if layer, ok := layers["output"]; ok {
 		memoryLayerOutput += layer.size()
 	} else if layer, ok := layers["token_embd"]; ok {
 		memoryLayerOutput += layer.size()
 	}
 
-	if gpus[0].Library == "metal" && opts.UseMMap {
-		// memory is preallocated for output tensors
-		memoryRequiredTotal += memoryLayerOutput
-		memoryRequiredPartial += memoryLayerOutput
+	// Output layer handled at the end if we have space
+	gpuZeroOverhead := projectorSize
+
+	// Reduce set of GPUs to only those that have sufficient space to fit overhead and at least one layer
+	var layerCount int
+	layerCounts := make([]int, len(gpus))
+	gpuAllocations := make([]uint64, len(gpus))
+	type gs struct {
+		i int
+		g *gpu.GpuInfo
+	}
+	gpusWithSpace := []gs{}
+	for i := range gpus {
+		var gzo uint64
+		if len(gpusWithSpace) == 0 {
+			gzo = gpuZeroOverhead
+		}
+		// Only include GPUs that can fit the graph, gpu minimum, the layer buffer and at least more layer
+		if gpus[i].FreeMemory < gzo+max(graphPartialOffload, graphFullOffload)+gpus[i].MinimumMemory+2*layerSize {
+			slog.Debug("gpu has too little memory to allocate any layers", "gpu", gpus[i])
+			continue
+		}
+		gpusWithSpace = append(gpusWithSpace, gs{i, &gpus[i]})
+		gpuAllocations[i] += gpus[i].MinimumMemory + layerSize // We hold off on graph until we know partial vs. full
 	}
 
-	var layerCount int
+	var gpuZeroID int
+	if len(gpusWithSpace) > 0 {
+		gpuZeroID = gpusWithSpace[0].i
+		gpuAllocations[gpuZeroID] += gpuZeroOverhead
+	}
+
+	// For all the layers, find where they can fit on the GPU(s)
 	for i := range int(ggml.KV().BlockCount()) {
+		// Some models have inconsistent layer sizes
 		if blk, ok := layers[fmt.Sprintf("blk.%d", i)]; ok {
-			memoryLayer := blk.size()
+			layerSize = blk.size()
+			layerSize += kv / ggml.KV().BlockCount()
+		}
+		memoryWeights += layerSize
 
-			// KV is proportional to the number of layers
-			memoryLayer += kv / ggml.KV().BlockCount()
+		if opts.NumGPU >= 0 && layerCount >= opts.NumGPU {
+			// Stop allocating on GPU(s) once we hit the users target NumGPU
+			continue
+		}
 
-			memoryRequiredTotal += memoryLayer
-			if (opts.NumGPU >= 0 && layerCount+1 <= opts.NumGPU) || (opts.NumGPU < 0 && memoryAvailable > memoryRequiredPartial+memoryLayer) {
-				memoryRequiredPartial += memoryLayer
+		// distribute the layers across the GPU(s) that have space
+		for j := len(gpusWithSpace); j > 0; j-- {
+			g := gpusWithSpace[i%j]
+			used := gpuAllocations[g.i] + max(graphPartialOffload, graphFullOffload)
+			if g.g.FreeMemory > used+layerSize {
+				gpuAllocations[g.i] += layerSize
+				layerCounts[g.i]++
 				layerCount++
+				break
+			} else {
+				gpusWithSpace = append(gpusWithSpace[:i%j], gpusWithSpace[i%j+1:]...)
 			}
 		}
 	}
-
-	if gpus[0].Library != "metal" || !opts.UseMMap {
-		// memory was not preallocated for output tensors
-		memoryRequiredTotal += memoryLayerOutput
+	if layerCount >= int(ggml.KV().BlockCount()) {
+		fullyLoaded = true
+	} else {
+		for i := layerCount; i < int(ggml.KV().BlockCount()); i++ {
+			overflow += layerSize
+		}
 	}
 
-	if (opts.NumGPU >= 0 && layerCount+1 <= opts.NumGPU) || (opts.NumGPU < 0 && memoryAvailable > memoryRequiredTotal) {
-		layerCount = int(ggml.KV().BlockCount()) + 1
-		memoryRequiredPartial = memoryRequiredTotal
+	// Determine if we need to consider output then find where it fits
+	if memoryLayerOutput > 0 && (opts.NumGPU < 0 || layerCount < opts.NumGPU) {
+		for j := len(gpusWithSpace); j > 0; j-- {
+			g := gpusWithSpace[layerCount%j]
+			used := gpuAllocations[g.i] + max(graphPartialOffload, graphFullOffload)
+			if g.g.FreeMemory > used+memoryLayerOutput {
+				gpuAllocations[g.i] += memoryLayerOutput
+				layerCounts[g.i]++
+				layerCount++
+				break
+			}
+		}
+
+		if layerCount < int(ggml.KV().BlockCount())+1 {
+			fullyLoaded = false
+			overflow += memoryLayerOutput
+		}
 	}
 
-	memoryWeights := memoryRequiredTotal - memoryMinimum - graphFullOffload - kv
+	// Add the applicable (full or partial) graph allocations
+	for i := range gpus {
+		if layerCounts[i] <= 0 {
+			continue
+		}
+		if fullyLoaded {
+			gpuAllocations[i] += graphFullOffload
+		} else {
+			gpuAllocations[i] += graphPartialOffload
+		}
+	}
+	if fullyLoaded {
+		graphOffload = graphFullOffload
+	} else {
+		graphOffload = graphPartialOffload
+	}
 
+	// Summaries for the log
+	var memoryRequiredPartial, memoryRequiredTotal uint64
+	for i := range gpuAllocations {
+		memoryRequiredPartial += gpuAllocations[i]
+	}
+	memoryRequiredTotal = memoryRequiredPartial + overflow
+
+	tensorSplit := ""
+	if len(gpus) > 1 {
+		splits := make([]string, len(gpus))
+		for i, count := range layerCounts {
+			splits[i] = strconv.Itoa(count)
+		}
+		tensorSplit = strings.Join(splits, ",")
+	}
+	allocationsList := []string{}
+	for _, a := range gpuAllocations {
+		allocationsList = append(allocationsList, format.HumanBytes2(a))
+	}
+
+	estimate := MemoryEstimate{
+		TotalSize: memoryRequiredTotal,
+		Layers:    0,
+		Graph:     0,
+		VRAMSize:  0,
+		GPUSizes:  []uint64{},
+
+		inferenceLibrary:    gpus[0].Library,
+		layersRequested:     opts.NumGPU,
+		layersModel:         int(ggml.KV().BlockCount()) + 1,
+		availableList:       availableList,
+		kv:                  kv,
+		allocationsList:     allocationsList,
+		memoryWeights:       memoryWeights,
+		memoryLayerOutput:   memoryLayerOutput,
+		graphFullOffload:    graphFullOffload,
+		graphPartialOffload: graphPartialOffload,
+	}
+
+	if gpus[0].Library == "cpu" {
+		return estimate
+	}
+	if layerCount == 0 {
+		slog.Debug("insufficient VRAM to load any model layers")
+		return estimate
+	}
+	estimate.Layers = layerCount
+	estimate.Graph = graphOffload
+	estimate.VRAMSize = memoryRequiredPartial
+	estimate.TotalSize = memoryRequiredTotal
+	estimate.TensorSplit = tensorSplit
+	estimate.GPUSizes = gpuAllocations
+	return estimate
+}
+
+func (m MemoryEstimate) log() {
 	slog.Info(
-		"offload to gpu",
+		"offload to "+m.inferenceLibrary,
 		slog.Group(
 			"layers",
 			// requested number of layers to offload
-			"requested", opts.NumGPU,
+			"requested", m.layersRequested,
+			// The number of layers the model has (including output)
+			"model", m.layersModel,
 			// estimated number of layers that can be offloaded
-			"real", layerCount,
+			"offload", m.Layers,
+			// multi-gpu split for tensors
+			"split", m.TensorSplit,
 		),
 		slog.Group(
 			"memory",
-			// memory available for offloading
-			"available", format.HumanBytes2(memoryAvailable),
+			// memory available by GPU for offloading
+			"available", m.availableList,
 			slog.Group(
 				"required",
 				// memory required for full offloading
-				"full", format.HumanBytes2(memoryRequiredTotal),
+				"full", format.HumanBytes2(m.TotalSize),
 				// memory required to offload layers.estimate layers
-				"partial", format.HumanBytes2(memoryRequiredPartial),
+				"partial", format.HumanBytes2(m.VRAMSize),
 				// memory of KV cache
-				"kv", format.HumanBytes2(kv),
+				"kv", format.HumanBytes2(m.kv),
+				// Allocations across the GPUs
+				"allocations", m.allocationsList,
 			),
 			slog.Group(
 				"weights",
 				// memory of the weights
-				"total", format.HumanBytes2(memoryWeights),
+				"total", format.HumanBytes2(m.memoryWeights),
 				// memory of repeating layers
-				"repeating", format.HumanBytes2(memoryWeights-memoryLayerOutput),
+				"repeating", format.HumanBytes2(m.memoryWeights-m.memoryLayerOutput),
 				// memory of non-repeating layers
-				"nonrepeating", format.HumanBytes2(memoryLayerOutput),
+				"nonrepeating", format.HumanBytes2(m.memoryLayerOutput),
 			),
 			slog.Group(
 				"graph",
 				// memory of graph when fully offloaded
-				"full", format.HumanBytes2(graphFullOffload),
+				"full", format.HumanBytes2(m.graphFullOffload),
 				// memory of graph when not fully offloaded
-				"partial", format.HumanBytes2(graphPartialOffload),
+				"partial", format.HumanBytes2(m.graphPartialOffload),
 			),
 		),
 	)
-	if gpus[0].Library == "cpu" {
-		return 0, 0, memoryRequiredTotal
-	}
-	if memoryRequiredPartial > memoryAvailable {
-		slog.Debug("insufficient VRAM to load any model layers")
-		return 0, 0, memoryRequiredTotal
-	}
-
-	return layerCount, memoryRequiredPartial, memoryRequiredTotal
 }

llm/memory_test.go

@@ -0,0 +1,130 @@
+package llm
+
+import (
+	"bytes"
+	"encoding/binary"
+	"fmt"
+	"os"
+	"testing"
+
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/gpu"
+	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
+)
+
+func TestEstimateGPULayers(t *testing.T) {
+	envconfig.Debug = true
+	modelName := "dummy"
+	f, err := os.CreateTemp(t.TempDir(), modelName)
+	require.NoError(t, err)
+	defer f.Close()
+	gguf := NewGGUFV3(binary.LittleEndian)
+	inputLayerCount := 5
+
+	tensors := []Tensor{
+		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
+		{Name: "blk.1.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
+		{Name: "blk.2.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
+		{Name: "blk.3.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
+		{Name: "blk.4.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
+		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
+	}
+	assert.Len(t, tensors, inputLayerCount+1)
+	err = gguf.Encode(f, KV{
+		"general.architecture":          "llama",
+		"general.name":                  "name",
+		"llama.context_length":          uint32(32),
+		"llama.embedding_length":        uint32(4096),
+		"llama.block_count":             uint32(inputLayerCount),
+		"llama.attention.head_count":    uint32(32),
+		"llama.attention.head_count_kv": uint32(32),
+		"tokenizer.ggml.tokens":         []string{" "},
+		"tokenizer.ggml.scores":         []float32{0},
+		"tokenizer.ggml.token_type":     []int32{0},
+	}, tensors)
+	require.NoError(t, err)
+
+	ggml, err := LoadModel(f.Name(), 0)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	// Simple CPU scenario
+	gpus := []gpu.GpuInfo{
+		{
+			Library: "cpu",
+		},
+	}
+	projectors := []string{}
+	opts := api.DefaultOptions()
+	t.Run("cpu", func(t *testing.T) {
+		estimate := EstimateGPULayers(gpus, ggml, projectors, opts)
+		assert.Equal(t, 0, estimate.Layers)
+		assert.Equal(t, uint64(0), estimate.Graph)
+	})
+
+	// derived from the dummy ggml file above
+	graphPartialOffload := uint64(202377216)
+	graphFullOffload := uint64(171968512)
+	layerSize := uint64(33554436)
+	projectorSize := uint64(0)
+	memoryLayerOutput := uint64(4)
+
+	// Dual CUDA scenario with assymetry
+	gpuMinimumMemory := uint64(2048)
+	gpus = []gpu.GpuInfo{
+		{
+			Library:       "cuda",
+			MinimumMemory: gpuMinimumMemory,
+		},
+		{
+			Library:       "cuda",
+			MinimumMemory: gpuMinimumMemory,
+		},
+	}
+	// Nested array: GPU0 layer space, GPU1 layer space, expected gpu0, expected gpu1
+	for i, s := range []struct {
+		layer0, layer1   uint64
+		expect0, expect1 uint64
+	}{
+		{1, 1, 1, 1},
+		{2, 1, 2, 1},
+		{2, 2, 2, 2},
+		{1, 2, 1, 2},
+		{3, 3, 3, 3},
+		{4, 4, 3, 3},
+		{6, 6, 3, 3},
+		{0, 3, 0, 3},
+	} {
+		t.Run(fmt.Sprintf("%v", s), func(t *testing.T) {
+			gpus[0].FreeMemory = 0
+			gpus[1].FreeMemory = 0
+			gpus[0].FreeMemory += projectorSize
+			if s.layer0 > 0 {
+				gpus[0].FreeMemory += memoryLayerOutput
+			} else {
+				gpus[1].FreeMemory += memoryLayerOutput
+			}
+			gpus[0].FreeMemory += gpuMinimumMemory + layerSize + s.layer0*layerSize + 1
+			gpus[1].FreeMemory += gpuMinimumMemory + layerSize + s.layer1*layerSize + 1
+			gpus[0].FreeMemory += max(graphFullOffload, graphPartialOffload)
+			gpus[1].FreeMemory += max(graphFullOffload, graphPartialOffload)
+			estimate := EstimateGPULayers(gpus, ggml, projectors, opts)
+			assert.Equal(t, int(s.expect0+s.expect1), estimate.Layers, "scenario %d: %v", i, s)
+			assert.Equal(t, fmt.Sprintf("%d,%d", s.expect0, s.expect1), estimate.TensorSplit, "scenario %d: %v", i, s)
+			var layerSums uint64
+			for _, b := range estimate.GPUSizes {
+				layerSums += b
+			}
+			if estimate.Layers < inputLayerCount+1 {
+				assert.Less(t, estimate.VRAMSize, estimate.TotalSize, "scenario %d: %v %+v", i, s, estimate)
+				assert.Equal(t, estimate.VRAMSize, layerSums, "scenario %d: %v %+v", i, s, estimate)
+			} else {
+				assert.Equal(t, estimate.VRAMSize, estimate.TotalSize, "scenario %d: %v %+v", i, s, estimate)
+				assert.Equal(t, estimate.TotalSize, layerSums, "scenario %d: %v %+v", i, s, estimate)
+			}
+		})
+	}
+}

llm/patches/01-load-progress.diff

@@ -1,8 +1,8 @@
 diff --git a/common/common.cpp b/common/common.cpp
-index ba1ecf0e..cead57cc 100644
+index 73ff0e85..6adb1a92 100644
 --- a/common/common.cpp
 +++ b/common/common.cpp
-@@ -1836,6 +1836,8 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
+@@ -2447,6 +2447,8 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
      mparams.use_mmap        = params.use_mmap;
      mparams.use_mlock       = params.use_mlock;
      mparams.check_tensors   = params.check_tensors;
@@ -12,20 +12,20 @@ index ba1ecf0e..cead57cc 100644
          mparams.kv_overrides = NULL;
      } else {
 diff --git a/common/common.h b/common/common.h
-index d80344f2..71e84834 100644
+index 58ed72f4..0bb2605e 100644
 --- a/common/common.h
 +++ b/common/common.h
-@@ -174,6 +174,13 @@ struct gpt_params {
-     // multimodal models (see examples/llava)
+@@ -180,6 +180,13 @@ struct gpt_params {
      std::string mmproj = "";        // path to multimodal projector
      std::vector<std::string> image; // path to image file(s)
-+
+ 
 +    // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
 +    // If the provided progress_callback returns true, model loading continues.
 +    // If it returns false, model loading is immediately aborted.
 +    llama_progress_callback progress_callback = NULL;
 +    // context pointer passed to the progress callback
 +    void * progress_callback_user_data;
- };
- 
- void gpt_params_handle_model_default(gpt_params & params);
++
+     // server params
+     int32_t port           = 8080;         // server listens on this network port
+     int32_t timeout_read   = 600;          // http read timeout in seconds

llm/patches/05-default-pretokenizer.diff

@@ -1,8 +1,8 @@
 diff --git a/llama.cpp b/llama.cpp
-index 40d2ec2c..74f3ee9c 100644
+index 61948751..4b72a293 100644
 --- a/llama.cpp
 +++ b/llama.cpp
-@@ -4642,16 +4642,7 @@ static void llm_load_vocab(
+@@ -4824,16 +4824,7 @@ static void llm_load_vocab(
  
          // for now, only BPE models have pre-tokenizers
          if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
@@ -15,14 +15,14 @@ index 40d2ec2c..74f3ee9c 100644
 -                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
 -                LLAMA_LOG_WARN("%s:                                             \n", __func__);
 -                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
--            } else if (
-+            if (
-                     tokenizer_pre == "default") {
+-            } else if (tokenizer_pre == "default") {
++            if (tokenizer_pre == "default") {
                  vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
              } else if (
-@@ -4703,7 +4694,8 @@ static void llm_load_vocab(
-                 tokenizer_pre == "smaug-bpe") {
-                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
+                     tokenizer_pre == "llama3"   ||
+@@ -4888,7 +4879,8 @@ static void llm_load_vocab(
+                 tokenizer_pre == "poro-chat") {
+                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
              } else {
 -                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
 +                LLAMA_LOG_WARN("%s: missing or unrecognized pre-tokenizer type, using: 'default'\n", __func__);

llm/patches/07-gemma.diff

@@ -0,0 +1,305 @@
+From 5cadb45f39d001ffbad95b690d6cf0abcb4a6d96 Mon Sep 17 00:00:00 2001
+From: Ollama maintainers <hello@ollama.com>
+Date: Wed, 26 Jun 2024 16:18:09 -0700
+Subject: [PATCH] Architecture support
+
+---
+ llama.cpp | 194 +++++++++++++++++++++++++++++++++++++++++++++++++++++-
+ 1 file changed, 193 insertions(+), 1 deletion(-)
+
+diff --git a/llama.cpp b/llama.cpp
+index 61948751..3b4196f5 100644
+--- a/llama.cpp
++++ b/llama.cpp
+@@ -217,6 +217,7 @@ enum llm_arch {
+     LLM_ARCH_INTERNLM2,
+     LLM_ARCH_MINICPM,
+     LLM_ARCH_GEMMA,
++    LLM_ARCH_GEMMA2,
+     LLM_ARCH_STARCODER2,
+     LLM_ARCH_MAMBA,
+     LLM_ARCH_XVERSE,
+@@ -255,6 +256,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+     { LLM_ARCH_INTERNLM2,       "internlm2"    },
+     { LLM_ARCH_MINICPM,         "minicpm"      },
+     { LLM_ARCH_GEMMA,           "gemma"        },
++    { LLM_ARCH_GEMMA2,          "gemma2"       },
+     { LLM_ARCH_STARCODER2,      "starcoder2"   },
+     { LLM_ARCH_MAMBA,           "mamba"        },
+     { LLM_ARCH_XVERSE,          "xverse"       },
+@@ -464,10 +466,12 @@ enum llm_tensor {
+     LLM_TENSOR_ATTN_NORM,
+     LLM_TENSOR_ATTN_NORM_2,
+     LLM_TENSOR_ATTN_OUT_NORM,
++    LLM_TENSOR_ATTN_POST_NORM,
+     LLM_TENSOR_ATTN_ROT_EMBD,
+     LLM_TENSOR_FFN_GATE_INP,
+     LLM_TENSOR_FFN_GATE_INP_SHEXP,
+     LLM_TENSOR_FFN_NORM,
++    LLM_TENSOR_FFN_POST_NORM,
+     LLM_TENSOR_FFN_GATE,
+     LLM_TENSOR_FFN_DOWN,
+     LLM_TENSOR_FFN_UP,
+@@ -960,6 +964,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
+             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+         },
+     },
++    {
++        LLM_ARCH_GEMMA2,
++        {
++            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
++            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
++            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
++            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
++            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
++            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
++            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
++            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
++            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
++            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
++            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
++            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
++            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
++        },
++    },
+     {
+         LLM_ARCH_STARCODER2,
+         {
+@@ -1941,6 +1963,8 @@ enum e_model {
+     MODEL_8x22B,
+     MODEL_16x12B,
+     MODEL_10B_128x3_66B,
++    MODEL_9B,
++    MODEL_27B,
+ };
+ 
+ static const size_t kiB = 1024;
+@@ -2114,6 +2138,7 @@ struct llama_layer {
+     struct ggml_tensor * attn_out_norm_b;
+     struct ggml_tensor * attn_q_a_norm;
+     struct ggml_tensor * attn_kv_a_norm;
++    struct ggml_tensor * attn_post_norm;
+ 
+     // attention
+     struct ggml_tensor * wq;
+@@ -2136,6 +2161,7 @@ struct llama_layer {
+     // normalization
+     struct ggml_tensor * ffn_norm;
+     struct ggml_tensor * ffn_norm_b;
++    struct ggml_tensor * ffn_post_norm;
+     struct ggml_tensor * layer_out_norm;
+     struct ggml_tensor * layer_out_norm_b;
+     struct ggml_tensor * ffn_norm_exps;
+@@ -4529,6 +4555,16 @@ static void llm_load_hparams(
+                 }
+             } break;
+         case LLM_ARCH_GEMMA:
++            {
++                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
++
++                switch (hparams.n_layer) {
++                    case 18: model.type = e_model::MODEL_9B; break;
++                    case 28: model.type = e_model::MODEL_27B; break;
++                    default: model.type = e_model::MODEL_UNKNOWN;
++               }
++            } break;
++        case LLM_ARCH_GEMMA2:
+             {
+                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ 
+@@ -6305,6 +6341,40 @@ static bool llm_load_tensors(
+                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                     }
+                 } break;
++            case LLM_ARCH_GEMMA2:
++                {
++                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
++
++                    // output
++                    model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
++                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
++
++                    const int64_t n_ff          = hparams.n_ff;
++                    const int64_t n_embd_head_k = hparams.n_embd_head_k;
++                    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
++                    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
++
++                    for (uint32_t i = 0; i < n_layer; ++i) {
++                        ggml_context * ctx_layer = ctx_for_layer(i);
++                        ggml_context * ctx_split = ctx_for_layer_split(i);
++
++                        auto & layer = model.layers[i];
++
++                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
++
++                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
++                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
++                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
++                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
++                        layer.attn_post_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
++
++                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
++                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
++                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
++                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
++                        layer.ffn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd});
++                    }
++                } break;
+             case LLM_ARCH_STARCODER2:
+                 {
+                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+@@ -10614,6 +10684,123 @@ struct llm_build_context {
+         return gf;
+     }
+ 
++    struct ggml_cgraph * build_gemma2() {
++        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
++
++        const int64_t n_embd_head_k = hparams.n_embd_head_k;
++
++        struct ggml_tensor * cur;
++        struct ggml_tensor * inpL;
++
++        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
++
++        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
++        cb(inpL, "inp_scaled", -1);
++
++        // inp_pos - contains the positions
++        struct ggml_tensor * inp_pos = build_inp_pos();
++
++        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
++        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
++
++        for (int il = 0; il < n_layer; ++il) {
++            // norm
++            cur = llm_build_norm(ctx0, inpL, hparams,
++                    model.layers[il].attn_norm, NULL,
++                    LLM_NORM_RMS, cb, il);
++            cb(cur, "attn_norm", il);
++
++            // self-attention
++            {
++                // compute Q and K and RoPE them
++                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
++                cb(Qcur, "Qcur", il);
++
++                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
++                cb(Kcur, "Kcur", il);
++
++                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
++                cb(Vcur, "Vcur", il);
++
++                Qcur = ggml_rope_ext(
++                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos, nullptr,
++                        n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale,
++                        ext_factor, attn_factor, beta_fast, beta_slow);
++                cb(Qcur, "Qcur", il);
++
++                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
++                cb(Qcur, "Qcur_scaled", il);
++
++                Kcur = ggml_rope_ext(
++                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
++                        n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale,
++                        ext_factor, attn_factor, beta_fast, beta_slow);
++                cb(Kcur, "Kcur", il);
++
++                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
++                        model.layers[il].wo, NULL,
++                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
++            }
++
++            if (il == n_layer - 1) {
++                // skip computing output for unused tokens
++                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
++                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
++                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
++            }
++
++            cur = llm_build_norm(ctx0, cur, hparams,
++                    model.layers[il].attn_post_norm, NULL,
++                    LLM_NORM_RMS, cb, il);
++            cb(cur, "attn_post_norm", il);
++
++            struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
++            cb(sa_out, "sa_out", il);
++
++            cur = llm_build_norm(ctx0, sa_out, hparams,
++                    model.layers[il].ffn_norm, NULL,
++                    LLM_NORM_RMS, cb, il);
++            cb(cur, "ffn_norm", il);
++
++            // feed-forward network
++            {
++                cur = llm_build_ffn(ctx0, cur,
++                        model.layers[il].ffn_up, NULL,
++                        model.layers[il].ffn_gate, NULL,
++                        model.layers[il].ffn_down, NULL,
++                        NULL,
++                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
++                cb(cur, "ffn_out", il);
++            }
++
++            cur = llm_build_norm(ctx0, cur, hparams,
++                model.layers[il].ffn_post_norm, NULL,
++                LLM_NORM_RMS, cb, -1);
++            cb(cur, "ffn_post_norm", -1);
++
++            cur = ggml_add(ctx0, cur, sa_out);
++            cb(cur, "l_out", il);
++
++            // input for next layer
++            inpL = cur;
++        }
++
++        cur = inpL;
++
++        cur = llm_build_norm(ctx0, cur, hparams,
++                model.output_norm, NULL,
++                LLM_NORM_RMS, cb, -1);
++        cb(cur, "result_norm", -1);
++
++        // lm_head
++        cur = ggml_mul_mat(ctx0, model.output, cur);
++        cb(cur, "result_output", -1);
++
++        ggml_build_forward_expand(gf, cur);
++
++        return gf;
++    }
++
+     struct ggml_cgraph * build_starcoder2() {
+         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+ 
+@@ -11847,6 +12034,10 @@ static struct ggml_cgraph * llama_build_graph(
+             {
+                 result = llm.build_gemma();
+             } break;
++        case LLM_ARCH_GEMMA2:
++            {
++                result = llm.build_gemma2();
++            } break;
+         case LLM_ARCH_STARCODER2:
+             {
+                 result = llm.build_starcoder2();
+@@ -16671,6 +16862,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
+         case LLM_ARCH_PHI2:
+         case LLM_ARCH_PHI3:
+         case LLM_ARCH_GEMMA:
++        case LLM_ARCH_GEMMA2:
+         case LLM_ARCH_STARCODER2:
+         case LLM_ARCH_GPTNEOX:
+             return LLAMA_ROPE_TYPE_NEOX;
+@@ -18551,7 +18743,7 @@ static int32_t llama_chat_apply_template_internal(
+         if (add_ass) {
+             ss << "<s>assistant\n";
+         }
+-    } else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
++    } else if (tmpl == "gemma" || tmpl == "gemma2" || tmpl.find("<start_of_turn>") != std::string::npos) {
+         // google/gemma-7b-it
+         std::string system_prompt = "";
+         for (auto message : chat) {
+-- 
+2.45.2
+

llm/payload.go

@@ -58,7 +58,7 @@ func availableServers() map[string]string {
 	}
 
 	// glob payloadsDir for files that start with ollama_
-	pattern := filepath.Join(payloadsDir, "*")
+	pattern := filepath.Join(payloadsDir, "*", "ollama_*")
 
 	files, err := filepath.Glob(pattern)
 	if err != nil {
@@ -69,7 +69,7 @@ func availableServers() map[string]string {
 	servers := make(map[string]string)
 	for _, file := range files {
 		slog.Debug("availableServers : found", "file", file)
-		servers[filepath.Base(file)] = file
+		servers[filepath.Base(filepath.Dir(file))] = filepath.Dir(file)
 	}
 
 	return servers
@@ -82,8 +82,8 @@ func serversForGpu(info gpu.GpuInfo) []string {
 	// glob workDir for files that start with ollama_
 	availableServers := availableServers()
 	requested := info.Library
-	if info.Variant != "" {
-		requested += "_" + info.Variant
+	if info.Variant != gpu.CPUCapabilityNone {
+		requested += "_" + info.Variant.String()
 	}
 
 	servers := []string{}
@@ -117,14 +117,14 @@ func serversForGpu(info gpu.GpuInfo) []string {
 
 	// Load up the best CPU variant if not primary requested
 	if info.Library != "cpu" {
-		variant := gpu.GetCPUVariant()
+		variant := gpu.GetCPUCapability()
 		// If no variant, then we fall back to default
 		// If we have a variant, try that if we find an exact match
 		// Attempting to run the wrong CPU instructions will panic the
 		// process
-		if variant != "" {
+		if variant != gpu.CPUCapabilityNone {
 			for cmp := range availableServers {
-				if cmp == "cpu_"+variant {
+				if cmp == "cpu_"+variant.String() {
 					servers = append(servers, cmp)
 					break
 				}
@@ -146,11 +146,11 @@ func serverForCpu() string {
 	if runtime.GOOS == "darwin" && runtime.GOARCH == "arm64" {
 		return "metal"
 	}
-	variant := gpu.GetCPUVariant()
+	variant := gpu.GetCPUCapability()
 	availableServers := availableServers()
-	if variant != "" {
+	if variant != gpu.CPUCapabilityNone {
 		for cmp := range availableServers {
-			if cmp == "cpu_"+variant {
+			if cmp == "cpu_"+variant.String() {
 				return cmp
 			}
 		}

llm/server.go

@@ -37,8 +37,9 @@ type LlamaServer interface {
 	Tokenize(ctx context.Context, content string) ([]int, error)
 	Detokenize(ctx context.Context, tokens []int) (string, error)
 	Close() error
-	EstimatedVRAM() uint64
+	EstimatedVRAM() uint64 // Total VRAM across all GPUs
 	EstimatedTotal() uint64
+	EstimatedVRAMByGPU(gpuID string) uint64
 }
 
 // llmServer is an instance of the llama.cpp server
@@ -49,18 +50,22 @@ type llmServer struct {
 	status  *StatusWriter
 	options api.Options
 
-	// TODO - this should be broken down by GPU
-	estimatedVRAM  uint64 // Estimated usage of VRAM by the loaded model
-	estimatedTotal uint64 // Total size of model
-	totalLayers    uint64
-	gpuCount       int
-	loadDuration   time.Duration // Record how long it took the model to load
-	loadProgress   float32
+	estimate    MemoryEstimate
+	totalLayers uint64
+	// gpuCount     int
+	gpus         gpu.GpuInfoList // Recorded just before the model loaded, free space will be incorrect
+	loadDuration time.Duration   // Record how long it took the model to load
+	loadProgress float32
 
 	sem *semaphore.Weighted
 }
 
-func LoadModel(model string) (*GGML, error) {
+// LoadModel will load a model from disk. The model must be in the GGML format.
+//
+// It collects array values for arrays with a size less than or equal to
+// maxArraySize. If maxArraySize is 0, the default value of 1024 is used. If
+// the maxArraySize is negative, all arrays are collected.
+func LoadModel(model string, maxArraySize int) (*GGML, error) {
 	if _, err := os.Stat(model); err != nil {
 		return nil, err
 	}
@@ -71,7 +76,7 @@ func LoadModel(model string) (*GGML, error) {
 	}
 	defer f.Close()
 
-	ggml, _, err := DecodeGGML(f)
+	ggml, _, err := DecodeGGML(f, maxArraySize)
 	return ggml, err
 }
 
@@ -80,43 +85,45 @@ func LoadModel(model string) (*GGML, error) {
 func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, projectors []string, opts api.Options) (LlamaServer, error) {
 	var err error
 	var cpuRunner string
-	var estimatedVRAM uint64
-	var estimatedTotal uint64
-	var systemMemory uint64
-	gpuCount := len(gpus)
-	if (len(gpus) == 1 && gpus[0].Library == "cpu") || opts.NumGPU == 0 {
-		// TODO evaluate system memory to see if we should block the load, or force an unload of another CPU runner
+	var estimate MemoryEstimate
+	var systemTotalMemory uint64
+	var systemFreeMemory uint64
 
-		cpuRunner = serverForCpu()
-		gpuCount = 0
-		_, _, estimatedTotal = EstimateGPULayers(gpus, ggml, projectors, opts)
+	systemMemInfo, err := gpu.GetCPUMem()
+	if err != nil {
+		slog.Error("failed to lookup system memory", "error", err)
 	} else {
-		if gpus[0].Library == "metal" {
-			memInfo, err := gpu.GetCPUMem()
-			if err != nil {
-				slog.Error("failed to lookup system memory", "error", err)
-			} else {
-				systemMemory = memInfo.TotalMemory
-				slog.Debug("system memory", "total", format.HumanBytes2(systemMemory))
-			}
-		}
-		var layers int
-		layers, estimatedVRAM, estimatedTotal = EstimateGPULayers(gpus, ggml, projectors, opts)
+		systemTotalMemory = systemMemInfo.TotalMemory
+		systemFreeMemory = systemMemInfo.FreeMemory
+		slog.Debug("system memory", "total", format.HumanBytes2(systemTotalMemory), "free", systemFreeMemory)
+	}
+
+	// If the user wants zero GPU layers, reset the gpu list to be CPU/system ram info
+	if opts.NumGPU == 0 {
+		gpus = gpu.GetCPUInfo()
+	}
+	if len(gpus) == 1 && gpus[0].Library == "cpu" {
+		cpuRunner = serverForCpu()
+		estimate = EstimateGPULayers(gpus, ggml, projectors, opts)
+	} else {
+		estimate = EstimateGPULayers(gpus, ggml, projectors, opts)
 
 		switch {
-		case gpus[0].Library == "metal" && estimatedVRAM > systemMemory:
+		case gpus[0].Library == "metal" && estimate.VRAMSize > systemTotalMemory:
 			// disable partial offloading when model is greater than total system memory as this
 			// can lead to locking up the system
 			opts.NumGPU = 0
-		case gpus[0].Library != "metal" && layers == 0:
+		case gpus[0].Library != "metal" && estimate.Layers == 0:
 			// Don't bother loading into the GPU if no layers can fit
 			cpuRunner = serverForCpu()
-			gpuCount = 0
-		case opts.NumGPU < 0 && layers > 0 && gpus[0].Library != "cpu":
-			opts.NumGPU = layers
+			gpus = gpu.GetCPUInfo()
+		case opts.NumGPU < 0 && estimate.Layers > 0 && gpus[0].Library != "cpu":
+			opts.NumGPU = estimate.Layers
 		}
 	}
 
+	estimate.log()
+
 	// Loop through potential servers
 	finalErr := errors.New("no suitable llama servers found")
 
@@ -159,6 +166,8 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 
 	params = append(params, "--log-disable")
 
+	params = append(params, "--timeout", fmt.Sprintf("%d", 600))
+
 	if opts.NumGPU >= 0 {
 		params = append(params, "--n-gpu-layers", fmt.Sprintf("%d", opts.NumGPU))
 	}
@@ -201,7 +210,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		if g.Library == "metal" &&
 			uint64(opts.NumGPU) > 0 &&
 			uint64(opts.NumGPU) < ggml.KV().BlockCount()+1 {
-			opts.UseMMap = false
+			opts.UseMMap = api.TriStateFalse
 		}
 	}
 
@@ -209,7 +218,11 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		params = append(params, "--flash-attn")
 	}
 
-	if !opts.UseMMap {
+	// Windows CUDA should not use mmap for best performance
+	// Linux  with a model larger than free space, mmap leads to thrashing
+	if (runtime.GOOS == "windows" && gpus[0].Library == "cuda" && opts.UseMMap == api.TriStateUndefined) ||
+		(runtime.GOOS == "linux" && systemFreeMemory < estimate.TotalSize && opts.UseMMap == api.TriStateUndefined) ||
+		opts.UseMMap == api.TriStateFalse {
 		params = append(params, "--no-mmap")
 	}
 
@@ -232,6 +245,14 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 
 	params = append(params, "--parallel", fmt.Sprintf("%d", numParallel))
 
+	if estimate.TensorSplit != "" {
+		params = append(params, "--tensor-split", estimate.TensorSplit)
+	}
+
+	if estimate.TensorSplit != "" {
+		params = append(params, "--tensor-split", estimate.TensorSplit)
+	}
+
 	for i := range len(servers) {
 		dir := availableServers[servers[i]]
 		if dir == "" {
@@ -242,8 +263,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		}
 
 		if strings.HasPrefix(servers[i], "cpu") {
-			// TODO if we tried a gpu runner first, and it failed, record the error and bubble that back up
-			gpuCount = 0
+			gpus = gpu.GetCPUInfo()
 		}
 
 		// Find an availableServers  port, retry on each iteration in case the failure was a port conflict race
@@ -265,8 +285,8 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		if runtime.GOOS == "windows" {
 			pathEnv = "PATH"
 		}
-		// prepend the server directory to LD_LIBRARY_PATH/PATH
-		libraryPaths := []string{dir}
+		// prepend the server directory to LD_LIBRARY_PATH/PATH and the parent dir for common dependencies
+		libraryPaths := []string{dir, filepath.Dir(dir)}
 
 		if libraryPath, ok := os.LookupEnv(pathEnv); ok {
 			// Append our runner directory to the path
@@ -299,22 +319,25 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		}
 
 		s := &llmServer{
-			port:           port,
-			cmd:            exec.Command(server, finalParams...),
-			status:         NewStatusWriter(os.Stderr),
-			options:        opts,
-			estimatedVRAM:  estimatedVRAM,
-			estimatedTotal: estimatedTotal,
-			sem:            semaphore.NewWeighted(int64(numParallel)),
-			totalLayers:    ggml.KV().BlockCount() + 1,
-			gpuCount:       gpuCount,
-			done:           make(chan error, 1),
+			port:        port,
+			cmd:         exec.Command(server, finalParams...),
+			status:      NewStatusWriter(os.Stderr),
+			options:     opts,
+			estimate:    estimate,
+			sem:         semaphore.NewWeighted(int64(numParallel)),
+			totalLayers: ggml.KV().BlockCount() + 1,
+			gpus:        gpus,
+			done:        make(chan error, 1),
 		}
 
 		s.cmd.Env = os.Environ()
 		s.cmd.Stdout = os.Stdout
 		s.cmd.Stderr = s.status
 
+		envWorkarounds := [][2]string{}
+		for _, gpu := range gpus {
+			envWorkarounds = append(envWorkarounds, gpu.EnvWorkarounds...)
+		}
 		visibleDevicesEnv, visibleDevicesEnvVal := gpus.GetVisibleDevicesEnv()
 		pathEnvVal := strings.Join(libraryPaths, string(filepath.ListSeparator))
 
@@ -329,6 +352,12 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 			} else if devicesNeeded && strings.EqualFold(cmp[0], visibleDevicesEnv) {
 				s.cmd.Env[i] = visibleDevicesEnv + "=" + visibleDevicesEnvVal
 				devicesNeeded = false
+			} else if len(envWorkarounds) != 0 {
+				for _, kv := range envWorkarounds {
+					if strings.EqualFold(cmp[0], kv[0]) {
+						s.cmd.Env[i] = kv[0] + "=" + kv[1]
+					}
+				}
 			}
 		}
 		if pathNeeded {
@@ -390,7 +419,7 @@ func projectorMemoryRequirements(filename string) uint64 {
 	}
 	defer file.Close()
 
-	ggml, _, err := DecodeGGML(file)
+	ggml, _, err := DecodeGGML(file, 0)
 	if err != nil {
 		return 0
 	}
@@ -1004,11 +1033,20 @@ func (s *llmServer) Close() error {
 }
 
 func (s *llmServer) EstimatedVRAM() uint64 {
-	return s.estimatedVRAM
+	return s.estimate.VRAMSize
 }
 
 func (s *llmServer) EstimatedTotal() uint64 {
-	return s.estimatedTotal
+	return s.estimate.TotalSize
+}
+
+func (s *llmServer) EstimatedVRAMByGPU(gpuID string) uint64 {
+	for i, gpu := range s.gpus {
+		if gpu.ID == gpuID {
+			return s.estimate.GPUSizes[i]
+		}
+	}
+	return 0
 }
 
 func parseDurationMs(ms float64) time.Duration {

openai/openai.go

@@ -178,9 +178,6 @@ func fromRequest(r ChatCompletionRequest) api.ChatRequest {
 
 	if r.Seed != nil {
 		options["seed"] = *r.Seed
-
-		// temperature=0 is required for reproducible outputs
-		options["temperature"] = 0.0
 	}
 
 	if r.FrequencyPenalty != nil {

scripts/build_windows.ps1

@@ -103,19 +103,19 @@ function buildApp() {
 function gatherDependencies() {
     write-host "Gathering runtime dependencies"
     cd "${script:SRC_DIR}"
-    md "${script:DEPS_DIR}" -ea 0 > $null
+    md "${script:DEPS_DIR}\ollama_runners" -ea 0 > $null
 
     # TODO - this varies based on host build system and MSVC version - drive from dumpbin output
     # currently works for Win11 + MSVC 2019 + Cuda V11
-    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\msvcp140.dll" "${script:DEPS_DIR}\"
-    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DEPS_DIR}\"
-    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DEPS_DIR}\"
+    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\msvcp140.dll" "${script:DEPS_DIR}\ollama_runners\"
+    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DEPS_DIR}\ollama_runners\"
+    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DEPS_DIR}\ollama_runners\"
 
 
     cp "${script:SRC_DIR}\app\ollama_welcome.ps1" "${script:SRC_DIR}\dist\"
     if ("${env:KEY_CONTAINER}") {
         write-host "about to sign"
-        foreach ($file in (get-childitem "${script:DEPS_DIR}/cu*.dll") + @("${script:SRC_DIR}\dist\ollama_welcome.ps1")){
+        foreach ($file in (get-childitem "${script:DEPS_DIR}\cuda\cu*.dll") + @("${script:SRC_DIR}\dist\ollama_welcome.ps1")){
             write-host "signing $file"
             & "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" `
                 /csp "Google Cloud KMS Provider" /kc ${env:KEY_CONTAINER} $file

scripts/install.sh

@@ -159,8 +159,8 @@ check_gpu() {
             esac ;;
         lshw)
             case $2 in
-                nvidia) available lshw && $SUDO lshw -c display -numeric | grep -q 'vendor: .* \[10DE\]' || return 1 ;;
-                amdgpu) available lshw && $SUDO lshw -c display -numeric | grep -q 'vendor: .* \[1002\]' || return 1 ;;
+                nvidia) available lshw && $SUDO lshw -c display -numeric -disable network | grep -q 'vendor: .* \[10DE\]' || return 1 ;;
+                amdgpu) available lshw && $SUDO lshw -c display -numeric -disable network | grep -q 'vendor: .* \[1002\]' || return 1 ;;
             esac ;;
         nvidia-smi) available nvidia-smi || return 1 ;;
     esac
@@ -279,7 +279,7 @@ if ! check_gpu nvidia-smi || [ -z "$(nvidia-smi | grep -o "CUDA Version: [0-9]*\
     case $OS_NAME in
         centos|rhel) install_cuda_driver_yum 'rhel' $(echo $OS_VERSION | cut -d '.' -f 1) ;;
         rocky) install_cuda_driver_yum 'rhel' $(echo $OS_VERSION | cut -c1) ;;
-        fedora) [ $OS_VERSION -lt '37' ] && install_cuda_driver_yum $OS_NAME $OS_VERSION || install_cuda_driver_yum $OS_NAME '37';;
+        fedora) [ $OS_VERSION -lt '39' ] && install_cuda_driver_yum $OS_NAME $OS_VERSION || install_cuda_driver_yum $OS_NAME '39';;
         amzn) install_cuda_driver_yum 'fedora' '37' ;;
         debian) install_cuda_driver_apt $OS_NAME $OS_VERSION ;;
         ubuntu) install_cuda_driver_apt $OS_NAME $(echo $OS_VERSION | sed 's/\.//') ;;

server/images.go

@@ -414,17 +414,22 @@ func CreateModel(ctx context.Context, name model.Name, modelFileDir, quantizatio
 							return err
 						}
 
-						layers, err := parseFromFile(ctx, temp, "", fn)
+						layer, err := NewLayer(temp, baseLayer.MediaType)
 						if err != nil {
 							return err
 						}
 
-						if len(layers) != 1 {
-							return errors.New("quantization failed")
+						if _, err := temp.Seek(0, io.SeekStart); err != nil {
+							return err
 						}
 
-						baseLayer.Layer = layers[0].Layer
-						baseLayer.GGML = layers[0].GGML
+						ggml, _, err := llm.DecodeGGML(temp, 0)
+						if err != nil {
+							return err
+						}
+
+						baseLayer.Layer = layer
+						baseLayer.GGML = ggml
 					}
 				}
 

server/model.go

@@ -11,6 +11,7 @@ import (
 	"net/http"
 	"os"
 	"path/filepath"
+	"strings"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/convert"
@@ -63,7 +64,7 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 			}
 			defer blob.Close()
 
-			ggml, _, err := llm.DecodeGGML(blob)
+			ggml, _, err := llm.DecodeGGML(blob, 0)
 			if err != nil {
 				return nil, err
 			}
@@ -77,62 +78,80 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 	return layers, nil
 }
 
-func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
+func extractFromZipFile(p string, file *os.File, fn func(api.ProgressResponse)) error {
 	stat, err := file.Stat()
 	if err != nil {
-		return nil, err
+		return err
 	}
 
 	r, err := zip.NewReader(file, stat.Size())
 	if err != nil {
-		return nil, err
+		return err
 	}
 
-	tempdir, err := os.MkdirTemp(filepath.Dir(file.Name()), "")
-	if err != nil {
-		return nil, err
-	}
-	defer os.RemoveAll(tempdir)
-
 	fn(api.ProgressResponse{Status: "unpacking model metadata"})
 	for _, f := range r.File {
+		n := filepath.Join(p, f.Name)
+		if !strings.HasPrefix(n, p) {
+			slog.Warn("skipped extracting file outside of context", "name", f.Name)
+			continue
+		}
+
+		if err := os.MkdirAll(filepath.Dir(n), 0o750); err != nil {
+			return err
+		}
+
 		// TODO(mxyng): this should not write out all files to disk
-		outfile, err := os.Create(filepath.Join(tempdir, f.Name))
+		outfile, err := os.Create(n)
 		if err != nil {
-			return nil, err
+			return err
 		}
 		defer outfile.Close()
 
 		infile, err := f.Open()
 		if err != nil {
-			return nil, err
+			return err
 		}
 		defer infile.Close()
 
 		if _, err = io.Copy(outfile, infile); err != nil {
-			return nil, err
+			return err
 		}
 
 		if err := outfile.Close(); err != nil {
-			return nil, err
+			return err
 		}
 
 		if err := infile.Close(); err != nil {
-			return nil, err
+			return err
 		}
 	}
 
-	mf, err := convert.GetModelFormat(tempdir)
+	return nil
+}
+
+func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
+	tempDir, err := os.MkdirTemp(filepath.Dir(file.Name()), "")
+	if err != nil {
+		return nil, err
+	}
+	defer os.RemoveAll(tempDir)
+
+	if err := extractFromZipFile(tempDir, file, fn); err != nil {
+		return nil, err
+	}
+
+	mf, err := convert.GetModelFormat(tempDir)
 	if err != nil {
 		return nil, err
 	}
 
-	params, err := mf.GetParams(tempdir)
+	params, err := mf.GetParams(tempDir)
 	if err != nil {
 		return nil, err
 	}
 
-	mArch, err := mf.GetModelArch("", tempdir, params)
+	mArch, err := mf.GetModelArch("", tempDir, params)
 	if err != nil {
 		return nil, err
 	}
@@ -150,7 +169,7 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
 
 	// TODO(mxyng): this should write directly into a layer
 	// e.g. NewLayer(arch.Reader(), "application/vnd.ollama.image.model")
-	temp, err := os.CreateTemp(tempdir, "fp16")
+	temp, err := os.CreateTemp(tempDir, "fp16")
 	if err != nil {
 		return nil, err
 	}
@@ -176,7 +195,7 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
 	}
 	defer bin.Close()
 
-	ggml, _, err := llm.DecodeGGML(bin)
+	ggml, _, err := llm.DecodeGGML(bin, 0)
 	if err != nil {
 		return nil, err
 	}
@@ -210,7 +229,7 @@ func parseFromFile(ctx context.Context, file *os.File, digest string, fn func(ap
 
 	var offset int64
 	for offset < stat.Size() {
-		ggml, n, err := llm.DecodeGGML(file)
+		ggml, n, err := llm.DecodeGGML(file, 0)
 		if errors.Is(err, io.EOF) {
 			break
 		} else if err != nil {

server/model_test.go

@@ -0,0 +1,92 @@
+package server
+
+import (
+	"archive/zip"
+	"bytes"
+	"io"
+	"os"
+	"path/filepath"
+	"slices"
+	"testing"
+
+	"github.com/ollama/ollama/api"
+)
+
+func createZipFile(t *testing.T, name string) *os.File {
+	t.Helper()
+
+	f, err := os.CreateTemp(t.TempDir(), "")
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	zf := zip.NewWriter(f)
+	defer zf.Close()
+
+	zh, err := zf.CreateHeader(&zip.FileHeader{Name: name})
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if _, err := io.Copy(zh, bytes.NewReader([]byte(""))); err != nil {
+		t.Fatal(err)
+	}
+
+	return f
+}
+
+func TestExtractFromZipFile(t *testing.T) {
+	cases := []struct {
+		name   string
+		expect []string
+	}{
+		{
+			name:   "good",
+			expect: []string{"good"},
+		},
+		{
+			name: filepath.Join("..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "bad"),
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			f := createZipFile(t, tt.name)
+			defer f.Close()
+
+			tempDir := t.TempDir()
+			if err := extractFromZipFile(tempDir, f, func(api.ProgressResponse) {}); err != nil {
+				t.Fatal(err)
+			}
+
+			var matches []string
+			if err := filepath.Walk(tempDir, func(p string, fi os.FileInfo, err error) error {
+				if err != nil {
+					return err
+				}
+
+				if !fi.IsDir() {
+					matches = append(matches, p)
+				}
+
+				return nil
+			}); err != nil {
+				t.Fatal(err)
+			}
+
+			var actual []string
+			for _, match := range matches {
+				rel, err := filepath.Rel(tempDir, match)
+				if err != nil {
+					t.Error(err)
+				}
+
+				actual = append(actual, rel)
+			}
+
+			if !slices.Equal(actual, tt.expect) {
+				t.Fatalf("expected %d files, got %d", len(tt.expect), len(matches))
+			}
+		})
+	}
+}

server/routes.go

@@ -646,9 +646,12 @@ func (s *Server) ShowModelHandler(c *gin.Context) {
 
 	resp, err := GetModelInfo(req)
 	if err != nil {
-		if os.IsNotExist(err) {
+		switch {
+		case os.IsNotExist(err):
 			c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found", req.Model)})
-		} else {
+		case err.Error() == "invalid model name":
+			c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
+		default:
 			c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		}
 		return
@@ -658,44 +661,55 @@ func (s *Server) ShowModelHandler(c *gin.Context) {
 }
 
 func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
-	model, err := GetModel(req.Model)
+	m, err := GetModel(req.Model)
 	if err != nil {
 		return nil, err
 	}
 
 	modelDetails := api.ModelDetails{
-		ParentModel:       model.ParentModel,
-		Format:            model.Config.ModelFormat,
-		Family:            model.Config.ModelFamily,
-		Families:          model.Config.ModelFamilies,
-		ParameterSize:     model.Config.ModelType,
-		QuantizationLevel: model.Config.FileType,
+		ParentModel:       m.ParentModel,
+		Format:            m.Config.ModelFormat,
+		Family:            m.Config.ModelFamily,
+		Families:          m.Config.ModelFamilies,
+		ParameterSize:     m.Config.ModelType,
+		QuantizationLevel: m.Config.FileType,
 	}
 
 	if req.System != "" {
-		model.System = req.System
+		m.System = req.System
 	}
 
 	if req.Template != "" {
-		model.Template = req.Template
+		m.Template = req.Template
 	}
 
 	msgs := make([]api.Message, 0)
-	for _, msg := range model.Messages {
+	for _, msg := range m.Messages {
 		msgs = append(msgs, api.Message{Role: msg.Role, Content: msg.Content})
 	}
 
+	n := model.ParseName(req.Model)
+	if !n.IsValid() {
+		return nil, fmt.Errorf("invalid model name")
+	}
+
+	manifest, err := ParseNamedManifest(n)
+	if err != nil {
+		return nil, err
+	}
+
 	resp := &api.ShowResponse{
-		License:  strings.Join(model.License, "\n"),
-		System:   model.System,
-		Template: model.Template,
-		Details:  modelDetails,
-		Messages: msgs,
+		License:    strings.Join(m.License, "\n"),
+		System:     m.System,
+		Template:   m.Template,
+		Details:    modelDetails,
+		Messages:   msgs,
+		ModifiedAt: manifest.fi.ModTime(),
 	}
 
 	var params []string
 	cs := 30
-	for k, v := range model.Options {
+	for k, v := range m.Options {
 		switch val := v.(type) {
 		case []interface{}:
 			for _, nv := range val {
@@ -709,20 +723,59 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 
 	for k, v := range req.Options {
 		if _, ok := req.Options[k]; ok {
-			model.Options[k] = v
+			m.Options[k] = v
 		}
 	}
 
 	var sb strings.Builder
 	fmt.Fprintln(&sb, "# Modelfile generated by \"ollama show\"")
 	fmt.Fprintln(&sb, "# To build a new Modelfile based on this, replace FROM with:")
-	fmt.Fprintf(&sb, "# FROM %s\n\n", model.ShortName)
-	fmt.Fprint(&sb, model.String())
+	fmt.Fprintf(&sb, "# FROM %s\n\n", m.ShortName)
+	fmt.Fprint(&sb, m.String())
 	resp.Modelfile = sb.String()
 
+	kvData, err := getKVData(m.ModelPath, req.Verbose)
+	if err != nil {
+		return nil, err
+	}
+	delete(kvData, "general.name")
+	delete(kvData, "tokenizer.chat_template")
+	resp.ModelInfo = kvData
+
+	if len(m.ProjectorPaths) > 0 {
+		projectorData, err := getKVData(m.ProjectorPaths[0], req.Verbose)
+		if err != nil {
+			return nil, err
+		}
+		resp.ProjectorInfo = projectorData
+	}
+
 	return resp, nil
 }
 
+func getKVData(digest string, verbose bool) (llm.KV, error) {
+	maxArraySize := 0
+	if verbose {
+		maxArraySize = -1
+	}
+	kvData, err := llm.LoadModel(digest, maxArraySize)
+	if err != nil {
+		return nil, err
+	}
+
+	kv := kvData.KV()
+
+	if !verbose {
+		for k := range kv {
+			if t, ok := kv[k].([]any); len(t) > 5 && ok {
+				kv[k] = []any{}
+			}
+		}
+	}
+
+	return kv, nil
+}
+
 func (s *Server) ListModelsHandler(c *gin.Context) {
 	ms, err := Manifests()
 	if err != nil {
@@ -1052,11 +1105,20 @@ func Serve(ln net.Listener) error {
 	schedCtx, schedDone := context.WithCancel(ctx)
 	sched := InitScheduler(schedCtx)
 	s := &Server{addr: ln.Addr(), sched: sched}
-	r := s.GenerateRoutes()
+
+	http.Handle("/", s.GenerateRoutes())
 
 	slog.Info(fmt.Sprintf("Listening on %s (version %s)", ln.Addr(), version.Version))
 	srvr := &http.Server{
-		Handler: r,
+		// Use http.DefaultServeMux so we get net/http/pprof for
+		// free.
+		//
+		// TODO(bmizerany): Decide if we want to make this
+		// configurable so it is not exposed by default, or allow
+		// users to bind it to a different port. This was a quick
+		// and easy way to get pprof, but it may not be the best
+		// way.
+		Handler: nil,
 	}
 
 	// listen for a ctrl+c and stop any loaded llm

server/routes_test.go

@@ -19,6 +19,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/types/model"
 	"github.com/ollama/ollama/version"
@@ -212,6 +213,7 @@ func Test_Routes(t *testing.T) {
 					"top_p 0.9",
 				}
 				assert.Equal(t, expectedParams, params)
+				assert.InDelta(t, 0, showResp.ModelInfo["general.parameter_count"], 1e-9, "Parameter count should be 0")
 			},
 		},
 	}
@@ -325,3 +327,40 @@ func TestCase(t *testing.T) {
 		})
 	}
 }
+
+func TestShow(t *testing.T) {
+	t.Setenv("OLLAMA_MODELS", t.TempDir())
+	envconfig.LoadConfig()
+
+	var s Server
+
+	createRequest(t, s.CreateModelHandler, api.CreateRequest{
+		Name: "show-model",
+		Modelfile: fmt.Sprintf(
+			"FROM %s\nFROM %s",
+			createBinFile(t, llm.KV{"general.architecture": "test"}, nil),
+			createBinFile(t, llm.KV{"general.architecture": "clip"}, nil),
+		),
+	})
+
+	w := createRequest(t, s.ShowModelHandler, api.ShowRequest{
+		Name: "show-model",
+	})
+
+	if w.Code != http.StatusOK {
+		t.Fatalf("expected status code 200, actual %d", w.Code)
+	}
+
+	var resp api.ShowResponse
+	if err := json.NewDecoder(w.Body).Decode(&resp); err != nil {
+		t.Fatal(err)
+	}
+
+	if resp.ModelInfo["general.architecture"] != "test" {
+		t.Fatal("Expected model architecture to be 'test', but got", resp.ModelInfo["general.architecture"])
+	}
+
+	if resp.ProjectorInfo["general.architecture"] != "clip" {
+		t.Fatal("Expected projector architecture to be 'clip', but got", resp.ProjectorInfo["general.architecture"])
+	}
+}

server/sched.go

@@ -7,7 +7,6 @@ import (
 	"log/slog"
 	"reflect"
 	"runtime"
-	"slices"
 	"sort"
 	"strings"
 	"sync"
@@ -27,6 +26,7 @@ type LlmRequest struct {
 	sessionDuration time.Duration
 	successCh       chan *runnerRef
 	errCh           chan error
+	schedAttempts   uint
 }
 
 type Scheduler struct {
@@ -38,9 +38,11 @@ type Scheduler struct {
 	loaded   map[string]*runnerRef
 	loadedMu sync.Mutex
 
-	loadFn      func(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList)
-	newServerFn func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options) (llm.LlamaServer, error)
-	getGpuFn    func() gpu.GpuInfoList
+	loadFn       func(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList)
+	newServerFn  func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options) (llm.LlamaServer, error)
+	getGpuFn     func() gpu.GpuInfoList
+	getCpuFn     func() gpu.GpuInfoList
+	reschedDelay time.Duration
 }
 
 var ErrMaxQueue = fmt.Errorf("server busy, please try again.  maximum pending requests exceeded")
@@ -54,6 +56,8 @@ func InitScheduler(ctx context.Context) *Scheduler {
 		loaded:        make(map[string]*runnerRef),
 		newServerFn:   llm.NewLlamaServer,
 		getGpuFn:      gpu.GetGPUInfo,
+		getCpuFn:      gpu.GetCPUInfo,
+		reschedDelay:  250 * time.Millisecond,
 	}
 	sched.loadFn = sched.load
 	return sched
@@ -105,6 +109,7 @@ func (s *Scheduler) processPending(ctx context.Context) {
 			return
 		case pending := <-s.pendingReqCh:
 			// Block other requests until we get this pending request running
+			pending.schedAttempts++
 
 			if pending.ctx.Err() != nil {
 				slog.Debug("pending request cancelled or timed out, skipping scheduling")
@@ -131,25 +136,36 @@ func (s *Scheduler) processPending(ctx context.Context) {
 				} else {
 					// Either no models are loaded or below envconfig.MaxRunners
 					// Get a refreshed GPU list
-					gpus := s.getGpuFn()
+					var gpus gpu.GpuInfoList
+					if pending.opts.NumGPU == 0 {
+						gpus = s.getCpuFn()
+					} else {
+						gpus = s.getGpuFn()
+					}
 
 					// Load model for fitting
-					ggml, err := llm.LoadModel(pending.model.ModelPath)
+					ggml, err := llm.LoadModel(pending.model.ModelPath, 0)
 					if err != nil {
 						pending.errCh <- err
 						break
 					}
 
-					// If we're CPU only mode, just limit by envconfig.MaxRunners above
-					// TODO handle system memory exhaustion
-					if (len(gpus) == 1 && gpus[0].Library == "cpu") || pending.opts.NumGPU == 0 {
-						slog.Debug("cpu mode with existing models, loading")
-						s.loadFn(pending, ggml, gpus)
-						break
-					}
-
-					// No models loaded. Load the model but prefer the best fit.
-					if loadedCount == 0 {
+					// Evaluate if the model will fit in the available system memory, or if we should unload a model first
+					if len(gpus) == 1 && gpus[0].Library == "cpu" {
+						if loadedCount == 0 {
+							slog.Debug("cpu mode with first model, loading")
+							s.loadFn(pending, ggml, gpus)
+							break
+						}
+						runnerToExpire = s.maybeFindCPURunnerToUnload(pending, ggml, gpus)
+						if runnerToExpire == nil {
+							slog.Debug("cpu mode with available system memory or first model, loading")
+							s.loadFn(pending, ggml, gpus)
+							break
+						}
+						// else we need to expire a runner
+					} else if loadedCount == 0 {
+						// No models loaded. Load the model but prefer the best fit.
 						slog.Debug("loading first model", "model", pending.model.ModelPath)
 						g := pickBestFitGPUs(pending, ggml, gpus)
 						if g != nil {
@@ -159,16 +175,44 @@ func (s *Scheduler) processPending(ctx context.Context) {
 						break
 					}
 
-					// More than one loaded model, so we have to see if the new one fits
-					// Update free memory from currently loaded models
-					s.updateFreeSpace(gpus)
-					gpus = pickBestFitGPUs(pending, ggml, gpus)
-					if gpus != nil {
-						slog.Debug("new model fits with existing models, loading")
-						s.loadFn(pending, ggml, gpus)
-						break
+					if runnerToExpire == nil {
+						// More than one loaded model, so we have to see if the
+						// new one fits
+						//
+						// We want to avoid loading on any GPUs that have other
+						// models still loading on them to avoid potential races
+						// with VRAM consumption ramping up during load
+						availGpus := s.filterGPUsWithoutLoadingModels(gpus)
+
+						// Update free memory from currently loaded models
+						s.updateFreeSpace(availGpus)
+						fitGpus := pickBestFitGPUs(pending, ggml, availGpus)
+						if fitGpus != nil {
+							slog.Debug("new model fits with existing models, loading")
+							s.loadFn(pending, ggml, fitGpus)
+							break
+						}
+
+						// We couldn't find a set of GPUs to fully load the new
+						// model. If no other models are loading (both GPU lists
+						// are the same) then we need to unload another model to
+						// make room
+						if len(availGpus) < len(gpus) {
+							// There are other requests pending, and this one
+							// needs more time, so put it on the back of the
+							// queue so that we might satisfy other pending
+							// requests that aren't blocked
+							go func() {
+								// Process in a go routine to avoid deadlocking
+								// the scheduler if our queue is full
+								slog.Debug("delaying scheduling while other models finish loading", "attempts", pending.schedAttempts, "model", pending.model.ModelPath)
+								time.Sleep(s.reschedDelay)
+								s.pendingReqCh <- pending
+							}()
+							break
+						}
+						runnerToExpire = s.findRunnerToUnload()
 					}
-					runnerToExpire = s.findRunnerToUnload()
 				}
 
 				if runnerToExpire == nil {
@@ -368,17 +412,9 @@ func (s *Scheduler) updateFreeSpace(allGpus gpu.GpuInfoList) {
 	s.loadedMu.Lock()
 	for _, r := range s.loaded {
 		r.refMu.Lock()
-		gpuIDs := make([]string, 0, len(r.gpus))
 		if r.llama != nil {
-			// TODO this should be broken down by GPU instead of assuming uniform spread
-			estimatedVRAMPerGPU := r.llama.EstimatedVRAM() / uint64(len(r.gpus))
-			for _, gpu := range r.gpus {
-				gpuIDs = append(gpuIDs, gpu.ID)
-			}
 			for _, gpu := range allGpus {
-				if slices.Contains(gpuIDs, gpu.ID) {
-					predMap[predKey{gpu.Library, gpu.ID}] += estimatedVRAMPerGPU
-				}
+				predMap[predKey{gpu.Library, gpu.ID}] += r.llama.EstimatedVRAMByGPU(gpu.ID)
 			}
 		} else {
 			slog.Warn("unexpected nil runner reference, memory prediction may be incorrect")
@@ -401,11 +437,36 @@ func (s *Scheduler) updateFreeSpace(allGpus gpu.GpuInfoList) {
 				// after we start our first runner, then we'll never acount for that, so picking the smallest free value seems prudent.
 				allGpus[i].FreeMemory = allGpus[i].TotalMemory - p
 			}
-			slog.Info("updated VRAM", "gpu", allGpus[i].ID, "library", allGpus[i].Library, "total", format.HumanBytes2(allGpus[i].TotalMemory), "available", format.HumanBytes2(allGpus[i].FreeMemory))
+			slog.Info("updated VRAM based on existing loaded models", "gpu", allGpus[i].ID, "library", allGpus[i].Library, "total", format.HumanBytes2(allGpus[i].TotalMemory), "available", format.HumanBytes2(allGpus[i].FreeMemory))
 		}
 	}
 }
 
+// While models are loading the VRAM consumption numbers will be indeterminate, so we have
+// to avoid scheduling another model on the same GPU(s) that haven't stabilized.
+// This routine returns the set of GPUs that do not have an active loading model.
+// If all GPUs have loading models, an empty list will be returned (not a single CPU entry)
+func (s *Scheduler) filterGPUsWithoutLoadingModels(allGpus gpu.GpuInfoList) gpu.GpuInfoList {
+	ret := append(gpu.GpuInfoList{}, allGpus...)
+	s.loadedMu.Lock()
+	defer s.loadedMu.Unlock()
+	for _, runner := range s.loaded {
+		if runner.loading {
+			slog.Debug("overlapping loads detected", "gpus", runner.gpus, "model", runner.modelPath)
+			for _, busyGPU := range runner.gpus {
+				for i := range ret {
+					if ret[i].ID == busyGPU.ID {
+						ret = append(ret[:i], ret[i+1:]...)
+						break
+					}
+				}
+			}
+		}
+	}
+	return ret
+}
+
+// TODO consolidate sched_types.go
 type runnerRef struct {
 	refMu sync.Mutex
 	// refCond   sync.Cond // Signaled on transition from 1 -> 0 refCount
@@ -487,8 +548,11 @@ func (runner *runnerRef) needsReload(ctx context.Context, req *LlmRequest) bool
 func (runner *runnerRef) waitForVRAMRecovery() chan interface{} {
 	finished := make(chan interface{}, 1)
 
-	// CPU or Metal don't need checking, so no waiting required, windows can page VRAM, and the APIs we query tend to be optimistic on free space
-	if (len(runner.gpus) == 1 && (runner.gpus[0].Library == "cpu" || runner.gpus[0].Library == "metal")) || runtime.GOOS == "windows" {
+	// CPU or Metal don't need checking, so no waiting required
+	// windows can page VRAM, only cuda currently can report accurate used vram usage
+	if len(runner.gpus) == 0 ||
+		(len(runner.gpus) == 1 && (runner.gpus[0].Library == "cpu" || runner.gpus[0].Library == "metal")) ||
+		(runtime.GOOS == "windows" && runner.gpus[0].Library != "cuda") {
 		finished <- struct{}{}
 		return finished
 	}
@@ -508,7 +572,7 @@ func (runner *runnerRef) waitForVRAMRecovery() chan interface{} {
 		for {
 			<-ticker.C
 			if time.Now().After(expiresAt) {
-				slog.Warn("gpu VRAM usage didn't recover within timeout", "seconds", time.Since(start).Seconds())
+				slog.Warn("gpu VRAM usage didn't recover within timeout", "seconds", time.Since(start).Seconds(), "model", runner.modelPath)
 				finished <- struct{}{}
 			}
 
@@ -521,7 +585,7 @@ func (runner *runnerRef) waitForVRAMRecovery() chan interface{} {
 			}
 			// If we're within ~80% of the estimated memory usage recovered, bail out
 			if float32(freeMemoryNow-freeMemoryBefore) > float32(runner.estimatedVRAM)*0.8 {
-				slog.Debug(fmt.Sprintf("gpu VRAM free memory converged after %0.2f seconds", time.Since(start).Seconds()))
+				slog.Debug(fmt.Sprintf("gpu VRAM free memory converged after %0.2f seconds", time.Since(start).Seconds()), "model", runner.modelPath)
 				finished <- struct{}{}
 				return
 			}
@@ -558,10 +622,12 @@ func pickBestFitGPUs(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList) gpu.
 		sort.Sort(sort.Reverse(gpu.ByFreeMemory(sgl)))
 
 		// First attempt to fit the model into a single GPU
-		for _, g := range sgl {
-			if ok, estimatedVRAM = llm.PredictServerFit([]gpu.GpuInfo{g}, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
-				slog.Debug("new model will fit in available VRAM in single GPU, loading", "model", req.model.ModelPath, "gpu", g.ID, "available", g.FreeMemory, "required", format.HumanBytes2(estimatedVRAM))
-				return []gpu.GpuInfo{g}
+		if !envconfig.SchedSpread {
+			for _, g := range sgl {
+				if ok, estimatedVRAM = llm.PredictServerFit([]gpu.GpuInfo{g}, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
+					slog.Debug("new model will fit in available VRAM in single GPU, loading", "model", req.model.ModelPath, "gpu", g.ID, "available", g.FreeMemory, "required", format.HumanBytes2(estimatedVRAM))
+					return []gpu.GpuInfo{g}
+				}
 			}
 		}
 
@@ -586,6 +652,10 @@ func (s *Scheduler) findRunnerToUnload() *runnerRef {
 		runnerList = append(runnerList, r)
 	}
 	s.loadedMu.Unlock()
+	if len(runnerList) == 0 {
+		slog.Debug("no loaded runner to unload")
+		return nil
+	}
 
 	// In the future we can enhance the algorithm to be smarter about picking the optimal runner to unload
 	// e.g., if we have multiple options, will one make room for the request?
@@ -616,3 +686,18 @@ func (s *Scheduler) unloadAllRunners() {
 		}
 	}
 }
+
+// If other runners are loaded, make sure the pending request will fit in system memory
+// If not, pick a runner to unload, else return nil and the request can be loaded
+func (s *Scheduler) maybeFindCPURunnerToUnload(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList) *runnerRef {
+	slog.Debug("evaluating if CPU model load will fit in available system memory")
+	estimate := llm.EstimateGPULayers(gpus, ggml, req.model.ProjectorPaths, req.opts)
+	if estimate.TotalSize <= gpus[0].FreeMemory {
+		slog.Debug("cpu inference mode, model fits in available system memory", "model", format.HumanBytes2(estimate.TotalSize), "available", format.HumanBytes2(gpus[0].FreeMemory))
+		return nil
+	}
+
+	// TODO - optimization: try to find CPU only runners first, or partial offloads with enough in system memory to make room
+
+	return s.findRunnerToUnload()
+}

server/sched_test.go

@@ -60,7 +60,7 @@ func TestLoad(t *testing.T) {
 	err := <-req.errCh
 	require.Contains(t, err.Error(), "this model may be incompatible")
 
-	server := &mockLlm{estimatedVRAM: 10}
+	server := &mockLlm{estimatedVRAM: 10, estimatedVRAMByGPU: map[string]uint64{}}
 	s.newServerFn = func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options) (llm.LlamaServer, error) {
 		return server, nil
 	}
@@ -128,13 +128,14 @@ func newScenario(t *testing.T, ctx context.Context, modelName string, estimatedV
 		"tokenizer.ggml.scores":         []float32{0},
 		"tokenizer.ggml.token_type":     []int32{0},
 	}, []llm.Tensor{
-		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: &bytes.Reader{}},
+		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
+		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 	})
 	require.NoError(t, err)
 
 	fname := f.Name()
 	model := &Model{Name: modelName, ModelPath: fname}
-	scenario.ggml, err = llm.LoadModel(model.ModelPath)
+	scenario.ggml, err = llm.LoadModel(model.ModelPath, 0)
 	require.NoError(t, err)
 
 	scenario.req = &LlmRequest{
@@ -145,17 +146,17 @@ func newScenario(t *testing.T, ctx context.Context, modelName string, estimatedV
 		successCh:       make(chan *runnerRef, 1),
 		errCh:           make(chan error, 1),
 	}
-	scenario.srv = &mockLlm{estimatedVRAM: estimatedVRAM}
+	scenario.srv = &mockLlm{estimatedVRAM: estimatedVRAM, estimatedVRAMByGPU: map[string]uint64{"": estimatedVRAM}}
 	return scenario
 }
 
 func TestRequests(t *testing.T) {
-	ctx, done := context.WithTimeout(context.Background(), time.Second)
+	ctx, done := context.WithTimeout(context.Background(), 10*time.Second)
 	defer done()
 
 	// Same model, same request
 	scenario1a := newScenario(t, ctx, "ollama-model-1", 10)
-	scenario1a.req.sessionDuration = 0
+	scenario1a.req.sessionDuration = 5 * time.Millisecond
 	scenario1b := newScenario(t, ctx, "ollama-model-1", 11)
 	scenario1b.req.model = scenario1a.req.model
 	scenario1b.ggml = scenario1a.ggml
@@ -166,6 +167,7 @@ func TestRequests(t *testing.T) {
 	tmpModel := *scenario1a.req.model
 	scenario2a.req.model = &tmpModel
 	scenario2a.ggml = scenario1a.ggml
+	scenario2a.req.sessionDuration = 5 * time.Millisecond
 
 	// Multiple loaded models
 	scenario3a := newScenario(t, ctx, "ollama-model-3a", 1*format.GigaByte)
@@ -181,6 +183,12 @@ func TestRequests(t *testing.T) {
 		g.FreeMemory = 12 * format.GigaByte
 		return []gpu.GpuInfo{g}
 	}
+	s.getCpuFn = func() gpu.GpuInfoList {
+		g := gpu.GpuInfo{Library: "cpu"}
+		g.TotalMemory = 32 * format.GigaByte
+		g.FreeMemory = 26 * format.GigaByte
+		return []gpu.GpuInfo{g}
+	}
 	s.newServerFn = scenario1a.newServer
 	slog.Info("scenario1a")
 	s.pendingReqCh <- scenario1a.req
@@ -309,7 +317,6 @@ func TestGetRunner(t *testing.T) {
 	ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond)
 	defer done()
 
-	// Same model, same request
 	scenario1a := newScenario(t, ctx, "ollama-model-1a", 10)
 	scenario1a.req.sessionDuration = 0
 	scenario1b := newScenario(t, ctx, "ollama-model-1b", 10)
@@ -419,7 +426,7 @@ func TestUseLoadedRunner(t *testing.T) {
 		sessionDuration: 2,
 	}
 	finished := make(chan *LlmRequest)
-	llm1 := &mockLlm{}
+	llm1 := &mockLlm{estimatedVRAMByGPU: map[string]uint64{}}
 	r1 := &runnerRef{llama: llm1, sessionDuration: 1}
 	req.useLoadedRunner(r1, finished)
 	require.Equal(t, uint(1), r1.refCount)
@@ -452,8 +459,8 @@ func TestUpdateFreeSpace(t *testing.T) {
 	gpus[0].FreeMemory = 900
 	gpus[1].TotalMemory = 2000
 	gpus[1].FreeMemory = 1900
-	llm1 := &mockLlm{estimatedVRAM: 100}
-	llm2 := &mockLlm{estimatedVRAM: 200}
+	llm1 := &mockLlm{estimatedVRAMByGPU: map[string]uint64{"1": 50, "2": 50}}
+	llm2 := &mockLlm{estimatedVRAMByGPU: map[string]uint64{"1": 125, "2": 75}}
 	r1 := &runnerRef{llama: llm1, gpus: gpus}
 	r2 := &runnerRef{llama: llm2, gpus: gpus}
 
@@ -464,8 +471,42 @@ func TestUpdateFreeSpace(t *testing.T) {
 	s.loadedMu.Unlock()
 
 	s.updateFreeSpace(gpus)
-	require.Equal(t, uint64(850), gpus[0].FreeMemory)
-	require.Equal(t, uint64(1850), gpus[1].FreeMemory)
+	require.Equal(t, uint64(1000-50-125), gpus[0].FreeMemory)
+	require.Equal(t, uint64(2000-50-75), gpus[1].FreeMemory)
+}
+
+func TestFilterGPUsWithoutLoadingModels(t *testing.T) {
+	ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond)
+	defer done()
+	gpus := gpu.GpuInfoList{
+		{
+			Library: "cuda",
+			ID:      "0",
+		},
+		{
+			Library: "cuda",
+			ID:      "1",
+		},
+	}
+	r1 := &runnerRef{gpus: gpu.GpuInfoList{gpus[0]}, loading: true}
+
+	s := InitScheduler(ctx)
+	s.loadedMu.Lock()
+	s.loaded["a"] = r1
+	s.loadedMu.Unlock()
+
+	tmp := s.filterGPUsWithoutLoadingModels(gpus)
+	require.Len(t, tmp, 1)
+	require.Equal(t, "1", tmp[0].ID)
+
+	r1.gpus = gpu.GpuInfoList{gpus[1]}
+	tmp = s.filterGPUsWithoutLoadingModels(gpus)
+	require.Len(t, tmp, 1)
+	require.Equal(t, "0", tmp[0].ID)
+
+	r1.gpus = gpu.GpuInfoList{}
+	tmp = s.filterGPUsWithoutLoadingModels(gpus)
+	require.Len(t, tmp, 2)
 }
 
 func TestFindRunnerToUnload(t *testing.T) {
@@ -492,7 +533,7 @@ func TestNeedsReload(t *testing.T) {
 	ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond)
 	defer done()
 
-	llm := &mockLlm{}
+	llm := &mockLlm{estimatedVRAMByGPU: map[string]uint64{}}
 	do := api.DefaultOptions()
 	runner := &runnerRef{
 		model:   &Model{AdapterPaths: []string{"adapter1"}, ProjectorPaths: []string{"projector1"}},
@@ -535,8 +576,8 @@ func TestUnloadAllRunners(t *testing.T) {
 	ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond)
 	defer done()
 
-	llm1 := &mockLlm{}
-	llm2 := &mockLlm{}
+	llm1 := &mockLlm{estimatedVRAMByGPU: map[string]uint64{}}
+	llm2 := &mockLlm{estimatedVRAMByGPU: map[string]uint64{}}
 	s := InitScheduler(ctx)
 	s.unloadAllRunners()
 
@@ -554,7 +595,7 @@ func TestUnloadAllRunners(t *testing.T) {
 }
 
 func TestUnload(t *testing.T) {
-	llm1 := &mockLlm{}
+	llm1 := &mockLlm{estimatedVRAMByGPU: map[string]uint64{}}
 	r1 := &runnerRef{llama: llm1}
 	r2 := &runnerRef{model: &Model{AdapterPaths: []string{"A"}}}
 	r1.unload()
@@ -564,19 +605,20 @@ func TestUnload(t *testing.T) {
 }
 
 type mockLlm struct {
-	pingResp          error
-	waitResp          error
-	completionResp    error
-	embeddingResp     []float64
-	embeddingRespErr  error
-	tokenizeResp      []int
-	tokenizeRespErr   error
-	detokenizeResp    string
-	detonekizeRespErr error
-	closeResp         error
-	closeCalled       bool
-	estimatedVRAM     uint64
-	estimatedTotal    uint64
+	pingResp           error
+	waitResp           error
+	completionResp     error
+	embeddingResp      []float64
+	embeddingRespErr   error
+	tokenizeResp       []int
+	tokenizeRespErr    error
+	detokenizeResp     string
+	detonekizeRespErr  error
+	closeResp          error
+	closeCalled        bool
+	estimatedVRAM      uint64
+	estimatedTotal     uint64
+	estimatedVRAMByGPU map[string]uint64
 }
 
 func (s *mockLlm) Ping(ctx context.Context) error             { return s.pingResp }
@@ -597,5 +639,6 @@ func (s *mockLlm) Close() error {
 	s.closeCalled = true
 	return s.closeResp
 }
-func (s *mockLlm) EstimatedVRAM() uint64  { return s.estimatedVRAM }
-func (s *mockLlm) EstimatedTotal() uint64 { return s.estimatedTotal }
+func (s *mockLlm) EstimatedVRAM() uint64                  { return s.estimatedVRAM }
+func (s *mockLlm) EstimatedTotal() uint64                 { return s.estimatedTotal }
+func (s *mockLlm) EstimatedVRAMByGPU(gpuid string) uint64 { return s.estimatedVRAMByGPU[gpuid] }

types/model/name.go

@@ -4,7 +4,6 @@ package model
 
 import (
 	"cmp"
-	"encoding/hex"
 	"errors"
 	"fmt"
 	"log/slog"
@@ -371,57 +370,3 @@ func cutPromised(s, sep string) (before, after string, ok bool) {
 	}
 	return cmp.Or(before, MissingPart), cmp.Or(after, MissingPart), true
 }
-
-type DigestType byte
-
-const (
-	DigestTypeInvalid DigestType = iota
-	DigestTypeSHA256
-)
-
-func (t DigestType) String() string {
-	switch t {
-	case DigestTypeSHA256:
-		return "sha256"
-	default:
-		return "invalid"
-	}
-}
-
-type Digest struct {
-	Type DigestType
-	Sum  [32]byte
-}
-
-func ParseDigest(s string) (Digest, error) {
-	i := strings.IndexAny(s, "-:")
-	if i < 0 {
-		return Digest{}, fmt.Errorf("invalid digest %q", s)
-	}
-	typ, encSum := s[:i], s[i+1:]
-	if typ != "sha256" {
-		return Digest{}, fmt.Errorf("unsupported digest type %q", typ)
-	}
-	d := Digest{
-		Type: DigestTypeSHA256,
-	}
-	n, err := hex.Decode(d.Sum[:], []byte(encSum))
-	if err != nil {
-		return Digest{}, err
-	}
-	if n != 32 {
-		return Digest{}, fmt.Errorf("digest %q decoded to %d bytes; want 32", encSum, n)
-	}
-	return d, nil
-}
-
-func (d Digest) String() string {
-	if d.Type == DigestTypeInvalid {
-		return ""
-	}
-	return fmt.Sprintf("sha256-%x", d.Sum)
-}
-
-func (d Digest) IsValid() bool {
-	return d.Type != DigestTypeInvalid
-}

types/model/name_test.go

@@ -284,40 +284,6 @@ func TestFilepathAllocs(t *testing.T) {
 	}
 }
 
-const (
-	validSha256    = "sha256-1000000000000000000000000000000000000000000000000000000000000000"
-	validSha256Old = "sha256:1000000000000000000000000000000000000000000000000000000000000000"
-)
-
-func TestParseDigest(t *testing.T) {
-	cases := []struct {
-		in   string
-		want string
-	}{
-		{"", ""},           // empty
-		{"sha123-12", ""},  // invalid type
-		{"sha256-", ""},    // invalid sum
-		{"sha256-123", ""}, // invalid odd length sum
-
-		{validSha256, validSha256},
-		{validSha256Old, validSha256},
-	}
-	for _, tt := range cases {
-		t.Run(tt.in, func(t *testing.T) {
-			got, err := ParseDigest(tt.in)
-			if err != nil {
-				if tt.want != "" {
-					t.Errorf("parseDigest(%q) = %v; want %v", tt.in, err, tt.want)
-				}
-				return
-			}
-			if got.String() != tt.want {
-				t.Errorf("parseDigest(%q).String() = %q; want %q", tt.in, got, tt.want)
-			}
-		})
-	}
-}
-
 func TestParseNameFromFilepath(t *testing.T) {
 	cases := map[string]Name{
 		filepath.Join("host", "namespace", "model", "tag"):      {Host: "host", Namespace: "namespace", Model: "model", Tag: "tag"},

util/bufioutil/buffer_seeker.go

@@ -0,0 +1,34 @@
+package bufioutil
+
+import (
+	"bufio"
+	"io"
+)
+
+type BufferedSeeker struct {
+	rs io.ReadSeeker
+	br *bufio.Reader
+}
+
+func NewBufferedSeeker(rs io.ReadSeeker, size int) *BufferedSeeker {
+	return &BufferedSeeker{
+		rs: rs,
+		br: bufio.NewReaderSize(rs, size),
+	}
+}
+
+func (b *BufferedSeeker) Read(p []byte) (int, error) {
+	return b.br.Read(p)
+}
+
+func (b *BufferedSeeker) Seek(offset int64, whence int) (int64, error) {
+	if whence == io.SeekCurrent {
+		offset -= int64(b.br.Buffered())
+	}
+	n, err := b.rs.Seek(offset, whence)
+	if err != nil {
+		return 0, err
+	}
+	b.br.Reset(b.rs)
+	return n, nil
+}

util/bufioutil/buffer_seeker_test.go

@@ -0,0 +1,64 @@
+package bufioutil
+
+import (
+	"bytes"
+	"io"
+	"strings"
+	"testing"
+)
+
+func TestBufferedSeeker(t *testing.T) {
+	const alphabet = "abcdefghijklmnopqrstuvwxyz"
+
+	bs := NewBufferedSeeker(strings.NewReader(alphabet), 0) // minReadBufferSize = 16
+
+	checkRead := func(buf []byte, expected string) {
+		t.Helper()
+		_, err := bs.Read(buf)
+		if err != nil {
+			t.Fatal(err)
+		}
+		if !bytes.Equal(buf, []byte(expected)) {
+			t.Fatalf("expected %s, got %s", expected, buf)
+		}
+	}
+
+	// Read the first 5 bytes
+	buf := make([]byte, 5)
+
+	checkRead(buf, "abcde")
+
+	// Seek back to the beginning
+	_, err := bs.Seek(0, io.SeekStart)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	// read 'a'
+	checkRead(buf[:1], "a")
+
+	if bs.br.Buffered() == 0 {
+		t.Fatalf("totally unexpected sanity check failed")
+	}
+
+	// Seek past 'b'
+	_, err = bs.Seek(1, io.SeekCurrent)
+	if err != nil {
+		t.Fatal(err)
+	}
+	checkRead(buf, "cdefg")
+
+	// Seek back to the beginning
+	_, err = bs.Seek(0, io.SeekStart)
+	if err != nil {
+		t.Fatal(err)
+	}
+	checkRead(buf, "abcde")
+
+	// Seek to the end
+	_, err = bs.Seek(-5, io.SeekEnd)
+	if err != nil {
+		t.Fatal(err)
+	}
+	checkRead(buf, "vwxyz")
+}