ggml: more accurate estimates for head count array case

Also standardized the approach by always treatting `HeadCount()` and
`HeadCountKV()` as arrays by filling them with the same value when
they're a scalar in the original GGUF

CONTRIBUTING.md

@@ -51,7 +51,7 @@ see if the change were accepted.
 
 The title should look like:
 
-   <package>: <short description>
+    <package>: <short description>
 
 The package is the most affected Go package. If the change does not affect Go
 code, then use the directory name instead. Changes to a single well-known

Dockerfile

@@ -104,8 +104,8 @@ COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
 FROM --platform=linux/arm64 scratch AS arm64
 COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
 COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
-COPY --from=jetpack-5 dist/lib/ollama/cuda_v11 lib/ollama/cuda_jetpack5
-COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 lib/ollama/cuda_jetpack6
+COPY --from=jetpack-5 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_jetpack5
+COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_jetpack6
 
 FROM scratch AS rocm
 COPY --from=rocm-6 dist/lib/ollama/rocm /lib/ollama/rocm

README.md

@@ -291,7 +291,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
 - [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
 - [Ollamac](https://github.com/kevinhermawan/Ollamac)
-- [big-AGI](https://github.com/enricoros/big-AGI/blob/main/docs/config-local-ollama.md)
+- [big-AGI](https://github.com/enricoros/big-AGI) 
 - [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
 - [Amica](https://github.com/semperai/amica)
 - [chatd](https://github.com/BruceMacD/chatd)
@@ -348,7 +348,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
 - [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
 - [PyOllaMx](https://github.com/kspviswa/pyOllaMx) - macOS application capable of chatting with both Ollama and Apple MLX models.
-- [Claude Dev](https://github.com/saoudrizwan/claude-dev) - VSCode extension for multi-file/whole-repo coding
+- [Cline](https://github.com/cline/cline) - Formerly known as Claude Dev is a VSCode extension for multi-file/whole-repo coding
 - [Cherry Studio](https://github.com/kangfenmao/cherry-studio) (Desktop client with Ollama support)
 - [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
 - [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
@@ -440,6 +440,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [DeepShell](https://github.com/Abyss-c0re/deepshell) Your self-hosted AI assistant. Interactive Shell, Files and Folders analysis.
 - [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
 - [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull and download models from Ollama Registry in your terminal.
+- [GGUF-to-Ollama](https://github.com/jonathanhecl/gguf-to-ollama) - Importing GGUF to Ollama made easy (multiplatform)
 
 ### Apple Vision Pro
 

api/types.go

@@ -163,19 +163,65 @@ func (t *ToolCallFunctionArguments) String() string {
 
 type Tool struct {
 	Type     string       `json:"type"`
+	Items    any          `json:"items,omitempty"`
 	Function ToolFunction `json:"function"`
 }
 
+// PropertyType can be either a string or an array of strings
+type PropertyType []string
+
+// UnmarshalJSON implements the json.Unmarshaler interface
+func (pt *PropertyType) UnmarshalJSON(data []byte) error {
+	// Try to unmarshal as a string first
+	var s string
+	if err := json.Unmarshal(data, &s); err == nil {
+		*pt = []string{s}
+		return nil
+	}
+
+	// If that fails, try to unmarshal as an array of strings
+	var a []string
+	if err := json.Unmarshal(data, &a); err != nil {
+		return err
+	}
+	*pt = a
+	return nil
+}
+
+// MarshalJSON implements the json.Marshaler interface
+func (pt PropertyType) MarshalJSON() ([]byte, error) {
+	if len(pt) == 1 {
+		// If there's only one type, marshal as a string
+		return json.Marshal(pt[0])
+	}
+	// Otherwise marshal as an array
+	return json.Marshal([]string(pt))
+}
+
+// String returns a string representation of the PropertyType
+func (pt PropertyType) String() string {
+	if len(pt) == 0 {
+		return ""
+	}
+	if len(pt) == 1 {
+		return pt[0]
+	}
+	return fmt.Sprintf("%v", []string(pt))
+}
+
 type ToolFunction struct {
 	Name        string `json:"name"`
 	Description string `json:"description"`
 	Parameters  struct {
 		Type       string   `json:"type"`
+		Defs       any      `json:"$defs,omitempty"`
+		Items      any      `json:"items,omitempty"`
 		Required   []string `json:"required"`
 		Properties map[string]struct {
-			Type        string   `json:"type"`
-			Description string   `json:"description"`
-			Enum        []string `json:"enum,omitempty"`
+			Type        PropertyType `json:"type"`
+			Items       any          `json:"items,omitempty"`
+			Description string       `json:"description"`
+			Enum        []any        `json:"enum,omitempty"`
 		} `json:"properties"`
 	} `json:"parameters"`
 }

api/types_test.go

@@ -231,3 +231,144 @@ func TestMessage_UnmarshalJSON(t *testing.T) {
 		}
 	}
 }
+
+func TestToolFunction_UnmarshalJSON(t *testing.T) {
+	tests := []struct {
+		name    string
+		input   string
+		wantErr string
+	}{
+		{
+			name: "valid enum with same types",
+			input: `{
+				"name": "test",
+				"description": "test function",
+				"parameters": {
+					"type": "object",
+					"required": ["test"],
+					"properties": {
+						"test": {
+							"type": "string",
+							"description": "test prop",
+							"enum": ["a", "b", "c"]
+						}
+					}
+				}
+			}`,
+			wantErr: "",
+		},
+		{
+			name: "empty enum array",
+			input: `{
+				"name": "test",
+				"description": "test function",
+				"parameters": {
+					"type": "object",
+					"required": ["test"],
+					"properties": {
+						"test": {
+							"type": "string",
+							"description": "test prop",
+							"enum": []
+						}
+					}
+				}
+			}`,
+			wantErr: "",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			var tf ToolFunction
+			err := json.Unmarshal([]byte(tt.input), &tf)
+
+			if tt.wantErr != "" {
+				require.Error(t, err)
+				assert.Contains(t, err.Error(), tt.wantErr)
+			} else {
+				require.NoError(t, err)
+			}
+		})
+	}
+}
+
+func TestPropertyType_UnmarshalJSON(t *testing.T) {
+	tests := []struct {
+		name     string
+		input    string
+		expected PropertyType
+	}{
+		{
+			name:     "string type",
+			input:    `"string"`,
+			expected: PropertyType{"string"},
+		},
+		{
+			name:     "array of types",
+			input:    `["string", "number"]`,
+			expected: PropertyType{"string", "number"},
+		},
+		{
+			name:     "array with single type",
+			input:    `["string"]`,
+			expected: PropertyType{"string"},
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			var pt PropertyType
+			if err := json.Unmarshal([]byte(test.input), &pt); err != nil {
+				t.Errorf("Unexpected error: %v", err)
+			}
+
+			if len(pt) != len(test.expected) {
+				t.Errorf("Length mismatch: got %v, expected %v", len(pt), len(test.expected))
+			}
+
+			for i, v := range pt {
+				if v != test.expected[i] {
+					t.Errorf("Value mismatch at index %d: got %v, expected %v", i, v, test.expected[i])
+				}
+			}
+		})
+	}
+}
+
+func TestPropertyType_MarshalJSON(t *testing.T) {
+	tests := []struct {
+		name     string
+		input    PropertyType
+		expected string
+	}{
+		{
+			name:     "single type",
+			input:    PropertyType{"string"},
+			expected: `"string"`,
+		},
+		{
+			name:     "multiple types",
+			input:    PropertyType{"string", "number"},
+			expected: `["string","number"]`,
+		},
+		{
+			name:     "empty type",
+			input:    PropertyType{},
+			expected: `[]`,
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			data, err := json.Marshal(test.input)
+			if err != nil {
+				t.Errorf("Unexpected error: %v", err)
+			}
+
+			if string(data) != test.expected {
+				t.Errorf("Marshaled data mismatch: got %v, expected %v", string(data), test.expected)
+			}
+		})
+	}
+}

cmd/cmd.go

@@ -1381,7 +1381,6 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_NOPRUNE"],
 				envVars["OLLAMA_ORIGINS"],
 				envVars["OLLAMA_SCHED_SPREAD"],
-				envVars["OLLAMA_TMPDIR"],
 				envVars["OLLAMA_FLASH_ATTENTION"],
 				envVars["OLLAMA_KV_CACHE_TYPE"],
 				envVars["OLLAMA_LLM_LIBRARY"],

docs/troubleshooting.md

@@ -26,7 +26,6 @@ When you run Ollama on **Windows**, there are a few different locations. You can
 - `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
 
 To enable additional debug logging to help troubleshoot problems, first **Quit the running app from the tray menu** then in a powershell terminal
 
@@ -69,10 +68,6 @@ If you run into problems on Linux and want to install an older version, or you'd
 curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
 ```
 
-## Linux tmp noexec 
-
-If your system is configured with the "noexec" flag where Ollama stores its temporary executable files, you can specify an alternate location by setting OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example OLLAMA_TMPDIR=/usr/share/ollama/
-
 ## Linux docker
 
 If Ollama initially works on the GPU in a docker container, but then switches to running on CPU after some period of time with errors in the server log reporting GPU discovery failures, this can be resolved by disabling systemd cgroup management in Docker.  Edit `/etc/docker/daemon.json` on the host and add `"exec-opts": ["native.cgroupdriver=cgroupfs"]` to the docker configuration.

docs/windows.md

@@ -62,7 +62,6 @@ the explorer window by hitting `<Ctrl>+R` and type in:
     - *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
-- `explorer %TEMP%` contains temporary executable files in one or more `ollama*` directories
 
 ## Uninstall
 

fs/ggml/ggml.go

@@ -6,6 +6,7 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
+	"reflect"
 	"slices"
 	"strings"
 
@@ -52,32 +53,80 @@ func (kv KV) EmbeddingLength() uint64 {
 	return uint64(kv.Uint("embedding_length"))
 }
 
-func (kv KV) HeadCount() uint64 {
-	return uint64(kv.Uint("attention.head_count"))
+func (kv KV) HeadCounts() []uint64 {
+	return kv.UintOrArrayAsArray("attention.head_count", kv.BlockCount(), 1)
 }
 
-func (kv KV) HeadCountKV() uint64 {
-	return uint64(kv.Uint("attention.head_count_kv", 1))
+func (kv KV) HeadCountKVs() []uint64 {
+	return kv.UintOrArrayAsArray("attention.head_count_kv", kv.BlockCount(), 1)
 }
 
-func (kv KV) EmbeddingHeadCount() uint64 {
-	if heads := kv.HeadCount(); heads > 0 {
-		return kv.EmbeddingLength() / heads
+func (kv KV) EmbeddingHeadCount() []uint64 {
+	headCount := kv.HeadCounts()
+	embeddingHeadCount := make([]uint64, len(headCount))
+	for i, heads := range headCount {
+		if heads == 0 {
+			embeddingHeadCount[i] = 0
+		} else {
+			embeddingHeadCount[i] = kv.EmbeddingLength() / heads
+		}
 	}
 
-	return 0
+	return embeddingHeadCount
 }
 
-func (kv KV) EmbeddingHeadCountK() uint64 {
-	return uint64(kv.Uint("attention.key_length", uint32(kv.EmbeddingHeadCount())))
+func (kv KV) FillArrayOrDefault(key string, defaultValue []uint64) []uint64 {
+	length := len(defaultValue)
+	if v, ok := keyValueUntyped(kv, key); ok {
+		switch v := v.(type) {
+		case uint32:
+			return FillArray(uint64(v), length)
+		case uint64:
+			return FillArray(v, length)
+		case int32:
+			return FillArray(uint64(v), length)
+		default:
+			slog.Warn("unsupported type", "key", key, "type", reflect.TypeOf(v))
+		}
+	}
+
+	return defaultValue
 }
 
-func (kv KV) EmbeddingHeadCountV() uint64 {
-	return uint64(kv.Uint("attention.value_length", uint32(kv.EmbeddingHeadCount())))
+func (kv KV) EmbeddingHeadCountK() []uint64 {
+	return kv.FillArrayOrDefault("attention.key_length", kv.EmbeddingHeadCount())
 }
 
-func (kv KV) GQA() uint64 {
-	return kv.HeadCount() / kv.HeadCountKV()
+func (kv KV) EmbeddingHeadCountV() []uint64 {
+	return kv.FillArrayOrDefault("attention.value_length", kv.EmbeddingHeadCount())
+}
+
+func (kv KV) GQAMax() uint64 {
+	heads := kv.HeadCounts()
+	headsKV := kv.HeadCountKVs()
+	if len(heads) != len(headsKV) {
+		slog.Warn("head count and head count kv are not the same length")
+		return 0
+	}
+	if len(heads) == 0 {
+		slog.Warn("head count is empty")
+		return 0
+	}
+
+	maxGQA := uint64(0)
+	for i := range heads {
+		head := heads[i]
+		headKV := headsKV[i]
+		if head == 0 || headKV == 0 {
+			return 0
+		}
+		gqa := head / headKV
+		if gqa > maxGQA {
+			maxGQA = gqa
+		}
+	}
+
+	return maxGQA
 }
 
 func (kv KV) ContextLength() uint64 {
@@ -104,6 +153,41 @@ func (kv KV) Bool(key string, defaultValue ...bool) bool {
 	return keyValue(kv, key, append(defaultValue, false)...)
 }
 
+func (kv KV) UintOrArrayAsArray(key string, n uint64, defaultSingleValue ...uint64) []uint64 {
+	var singleValue *uint64
+	if v, ok := keyValueUntyped(kv, key); ok {
+		switch v := v.(type) {
+		case *array:
+			switch v.values[0].(type) {
+			case int32, uint32, uint64:
+				values, ok := AsUint64Array(v.values)
+				if ok {
+					return values
+				}
+			default:
+				slog.Warn("unexpected array value type", "key", key, "type", reflect.TypeOf(v))
+			}
+		case uint32:
+			val := uint64(v)
+			singleValue = &val
+		case int32:
+			val := uint64(v)
+			singleValue = &val
+		}
+	}
+	if singleValue == nil {
+		slog.Warn("falling back to default")
+		singleValue = &defaultSingleValue[0]
+	}
+
+	values := make([]uint64, n)
+	for i := range values {
+		values[i] = *singleValue
+	}
+
+	return values
+}
+
 func (kv KV) Strings(key string, defaultValue ...[]string) []string {
 	r := keyValue(kv, key, &array{})
 	s := make([]string, r.size)
@@ -141,11 +225,7 @@ func (kv KV) OllamaEngineRequired() bool {
 }
 
 func keyValue[T string | uint32 | uint64 | float32 | *array | bool](kv KV, key string, defaultValue ...T) T {
-	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
-		key = kv.Architecture() + "." + key
-	}
-
-	if val, ok := kv[key]; ok {
+	if val, ok := keyValueUntyped(kv, key); ok {
 		return val.(T)
 	}
 
@@ -153,6 +233,18 @@ func keyValue[T string | uint32 | uint64 | float32 | *array | bool](kv KV, key s
 	return defaultValue[0]
 }
 
+func keyValueUntyped(kv KV, key string) (any, bool) {
+	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
+		key = kv.Architecture() + "." + key
+	}
+
+	if val, ok := kv[key]; ok {
+		return val, true
+	}
+
+	return nil, false
+}
+
 type Tensors struct {
 	items  []*Tensor
 	Offset uint64
@@ -418,12 +510,22 @@ func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
 
 func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType string) (kv []uint64, partialOffload, fullOffload uint64) {
 	embedding := f.KV().EmbeddingLength()
-	heads := f.KV().HeadCount()
-	headsKV := f.KV().HeadCountKV()
+	heads := f.KV().HeadCounts()
+	headsKV := f.KV().HeadCountKVs()
 	vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array).size)
 
 	embeddingHeads := f.KV().EmbeddingHeadCount()
+	maxEmbeddingHeads, ok := MaxValue(embeddingHeads)
+	if !ok {
+		maxEmbeddingHeads = 1
+		slog.Warn("failed to get max embedding heads")
+	}
 	embeddingHeadsK := f.KV().EmbeddingHeadCountK()
+	maxEmbeddingHeadsK, ok := MaxValue(embeddingHeadsK)
+	if !ok {
+		maxEmbeddingHeadsK = 1
+		slog.Warn("failed to get max embedding headsK")
+	}
 	embeddingHeadsV := f.KV().EmbeddingHeadCountV()
 
 	layers := f.Tensors().GroupLayers()
@@ -431,19 +533,30 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	bytesPerElement := kvCacheBytesPerElement(kvCacheType)
 	kv = make([]uint64, f.KV().BlockCount())
 	for i := range kv {
-		kv[i] = uint64(float64(context*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
+		kv[i] = uint64(float64(context*(embeddingHeadsK[i]+embeddingHeadsV[i])*headsKV[i]) * bytesPerElement)
+	}
+
+	maxHeads, ok := MaxValue(heads)
+	if !ok {
+		maxHeads = 1
+		slog.Warn("failed to get max heads")
+	}
+	maxHeadsKV, ok := MaxValue(headsKV)
+	if !ok {
+		maxHeadsKV = 1
+		slog.Warn("failed to get max headsKV")
 	}
 
 	switch f.KV().Architecture() {
 	case "llama":
 		fullOffload = max(
-			4*batch*(1+4*embedding+context*(1+heads)),
+			4*batch*(1+4*embedding+context*(1+maxHeads)),
 			4*batch*(embedding+vocab),
 		)
 
 		partialOffload = 4 * batch * embedding
 		partialOffload += max(
-			4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embeddingHeads*headsKV),
+			4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*maxHeads+maxEmbeddingHeads*maxHeadsKV),
 			4*batch*(embedding+vocab)+embedding*vocab*105/128,
 		)
 
@@ -451,16 +564,16 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 			// mixtral 8x22b
 			ff := uint64(f.KV()["llama.feed_forward_length"].(uint32))
 			partialOffload = max(
-				3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embeddingHeads*headsKV),
-				4*(context*batch*heads+context*embeddingHeads*headsKV+batch*1024+embeddingHeads*headsKV*batch),
+				3*ffnGateExpsWeight.Size()+4*batch*(2*ff+maxHeadsKV+embedding+context+maxEmbeddingHeads*maxHeadsKV),
+				4*(context*batch*maxHeads+context*maxEmbeddingHeads*maxHeadsKV+batch*1024+maxEmbeddingHeads*maxHeadsKV*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)
+			fullOffload = 4 * batch * (2 + 3*embedding + context*(1+maxHeads) + 2*maxHeadsKV + ffnGateWeight1)
 			partialOffload = max(
-				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),
+				4*batch*(3+maxEmbeddingHeads*maxHeadsKV+embedding+context*(1+maxHeads)+ffnGateWeight1)+(embedding*embedding+3*embedding*maxHeadsKV*ffnGateWeight1)*9/16,
+				4*batch*(1+2*embedding+context*(1+maxHeads))+embedding*(6*context*maxHeadsKV/maxHeads+embedding*9/16),
 			)
 		}
 	case "mllama":
@@ -469,7 +582,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 		crossAttentionLayers := f.KV().Uints("attention.cross_attention_layers")
 		for i := range kv {
 			if slices.Contains(crossAttentionLayers, uint32(i)) {
-				kv[i] = headsKV * (embeddingHeadsK + embeddingHeadsV) *
+				kv[i] = headsKV[i] * (embeddingHeadsK[i] + embeddingHeadsV[i]) *
 					4 * // sizeof(float32)
 					visionTokens *
 					tiles
@@ -477,7 +590,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 		}
 
 		fullOffload = max(
-			4*batch*(2+3*embedding+embeddingHeadsK*heads+context*(1+heads)),
+			4*batch*(2+3*embedding+maxEmbeddingHeadsK*maxHeads+context*(1+maxHeads)),
 			// vocab graph
 			4*batch*(embedding+vocab),
 		)
@@ -491,23 +604,23 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 
 		partialOffload = max(
 			4*(batch*
-				(2*embedding+1+context*(1+heads)+embeddingHeadsK*heads)+
+				(2*embedding+1+context*(1+maxHeads)+maxEmbeddingHeadsK*maxHeads)+
 				ropeFreqsCount+
-				embeddingHeadsK*context*headsKV),
+				maxEmbeddingHeadsK*context*maxHeadsKV),
 			// vocab graph
 			4*batch*(embedding+vocab)+embedding*vocab*105/128,
 		)
 	case "gemma", "gemma2", "gemma3":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
-			4*batch*(2+context+context*heads+2*embedding+2*embeddingHeadsK*heads),
+			4*batch*(2+context+context*maxHeads+2*embedding+2*maxEmbeddingHeadsK*maxHeads),
 		)
 
 		partialOffload = max(
 			4*embedding*batch+embedding*vocab*105/128+4*vocab*batch,
-			4*batch*(2*embedding+1+2*embeddingHeadsK*heads+context+context*heads)+
-				4*embeddingHeadsK*context*8+
-				embedding*embeddingHeadsK*heads*9/16,
+			4*batch*(2*embedding+1+2*maxEmbeddingHeadsK*maxHeads+context+context*maxHeads)+
+				4*maxEmbeddingHeadsK*context*8+
+				embedding*embedding*maxEmbeddingHeadsK*maxHeads*9/16,
 		)
 
 		// Gemma2 also has sliding window attention but we only have an optimized implementation in the Ollama
@@ -519,42 +632,42 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 				// Every 6th layer is a global layer, which is the full context size that has already been set. The other
 				// layers are the smaller local (sliding) layers.
 				if (i+1)%gemma3GlobalCacheCount != 0 {
-					kv[i] = uint64(float64(slidingWindow*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
+					kv[i] = uint64(float64(slidingWindow*(embeddingHeadsK[i]+embeddingHeadsV[i])*headsKV[i]) * bytesPerElement)
 				}
 			}
 		}
 	case "command-r":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
-			4*batch*(2+4*embedding+context*(1+heads)),
+			4*batch*(2+4*embedding+context*(1+maxHeads)),
 		)
 
 		partialOffload = max(
 			4*batch*(embedding+vocab)+embedding*vocab*105/128,
-			4*batch*(1+2*embedding+context*(1+heads))+4*embedding*context+embedding*embedding*9/16,
+			4*batch*(1+2*embedding+context*(1+maxHeads))+4*embedding*context+embedding*embedding*9/16,
 		)
 	case "qwen2":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
-			4*batch*(1+2*embedding+context+context*heads),
+			4*batch*(1+2*embedding+context+context*maxHeads),
 		)
 
 		partialOffload = max(
 			4*batch*(embedding+vocab)+embedding*vocab*105/128,
-			4*(batch*(1+2*embedding+context*(1+heads))+embedding*(1+context)),
+			4*(batch*(1+2*embedding+context*(1+maxHeads))+embedding*(1+context)),
 		)
 	case "phi2":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
-			4*batch*(1+4*embedding+context+context*heads),
+			4*batch*(1+4*embedding+context+context*maxHeads),
 		)
 
 		partialOffload = max(
 			4*batch*(2*embedding+vocab)+embedding*vocab*105/128,
-			4*batch*(2+3*embedding+context+context*heads),
+			4*batch*(2+3*embedding+context+context*maxHeads),
 		)
 	case "stablelm":
-		fullOffload = 4 * batch * (context*(1+heads) + 3*embedding + 2)
+		fullOffload = 4 * batch * (context*(1+maxHeads) + 3*embedding + 2)
 		partialOffload = max(
 			4*batch*(vocab+2*embedding),
 			fullOffload,
@@ -562,12 +675,12 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	case "deepseek2":
 		fullOffload = max(
 			4*batch*(3*embedding+vocab),
-			4*batch*(3*embedding+2+context*(1+headsKV)+2*embeddingHeadsK*headsKV),
+			4*batch*(3*embedding+2+context*(1+maxHeadsKV)+2*maxEmbeddingHeadsK*maxHeadsKV),
 		)
 
 		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,
+			4*batch*(2*embedding+1+2*maxEmbeddingHeadsK*maxHeadsKV+context+context*maxHeadsKV)+4*maxEmbeddingHeadsK*context*maxHeadsKV+embedding*embedding*maxEmbeddingHeadsK*maxHeadsKV*9/16,
 		)
 	case "chatglm":
 		fullOffload = 4 * batch * (embedding + vocab)
@@ -578,8 +691,8 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 				4*batch*(2+
 					2*embedding+
 					context+
-					context*heads+
-					embeddingHeadsK*heads+
+					context*maxHeads+
+					maxEmbeddingHeadsK*maxHeads+
 					qkvBias.Shape[0]),
 			)
 
@@ -587,11 +700,11 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 				partialOffload,
 				4*batch*(1+
 					2*embedding+
-					embeddingHeadsK*heads+
+					maxEmbeddingHeadsK*maxHeads+
 					context+
-					context*heads)+
-					4*embeddingHeadsK*context+
-					4*context*embeddingHeadsK+
+					context*maxHeads)+
+					4*maxEmbeddingHeadsK*context+
+					4*context*maxEmbeddingHeadsK+
 					4*qkvBias.Shape[0],
 			)
 		}
@@ -663,9 +776,15 @@ func (f GGML) SupportsFlashAttention() bool {
 	}
 
 	// Check head counts match and are non-zero
-	headCountK := f.KV().EmbeddingHeadCountK()
-	headCountV := f.KV().EmbeddingHeadCountV()
-	return headCountK != 0 && headCountV != 0 && headCountK == headCountV
+	headCount := f.KV().HeadCounts()
+	embeddingHeadCountK := f.KV().EmbeddingHeadCountK()
+	embeddingHeadCountV := f.KV().EmbeddingHeadCountV()
+	for i := range headCount {
+		if embeddingHeadCountK[i] != embeddingHeadCountV[i] {
+			return false
+		}
+	}
+	return true
 }
 
 // kvCacheBytesPerElement returns the number of bytes per element for a given KV cache type
@@ -679,3 +798,54 @@ func kvCacheBytesPerElement(cacheType string) float64 {
 		return 2 // f16 (default)
 	}
 }
+
+func AsUint64Array(v []any) ([]uint64, bool) {
+	switch v[0].(type) {
+	case uint32:
+		values := make([]uint64, len(v))
+		for i, v := range v {
+			values[i] = uint64(v.(uint32))
+		}
+		return values, true
+	case uint64:
+		values := make([]uint64, len(v))
+		for i, v := range v {
+			values[i] = v.(uint64)
+		}
+		return values, true
+	case int32:
+		values := make([]uint64, len(v))
+		for i, val := range v {
+			val := val.(int32)
+			if val < 0 {
+				slog.Warn("negative value in int32 array", "value", val)
+				return nil, false
+			}
+			values[i] = uint64(val)
+		}
+		return values, true
+	}
+	return nil, false
+}
+
+func MaxValue(values []uint64) (uint64, bool) {
+	if len(values) == 0 {
+		return 0, false
+	}
+
+	max := values[0]
+	for _, v := range values {
+		if v > max {
+			max = v
+		}
+	}
+	return max, true
+}
+
+func FillArray[T any](value T, n int) []T {
+	values := make([]T, n)
+	for i := range values {
+		values[i] = value
+	}
+	return values
+}

integration/max_queue_test.go

@@ -52,8 +52,8 @@ func TestMaxQueue(t *testing.T) {
 	embedCtx := ctx
 
 	var genwg sync.WaitGroup
+	genwg.Add(1)
 	go func() {
-		genwg.Add(1)
 		defer genwg.Done()
 		slog.Info("Starting generate request")
 		DoGenerate(ctx, t, client, req, resp, 45*time.Second, 5*time.Second)
@@ -71,8 +71,8 @@ func TestMaxQueue(t *testing.T) {
 	counterMu := sync.Mutex{}
 	var embedwg sync.WaitGroup
 	for i := 0; i < threadCount; i++ {
+		embedwg.Add(1)
 		go func(i int) {
-			embedwg.Add(1)
 			defer embedwg.Done()
 			slog.Info("embed started", "id", i)
 			embedReq := api.EmbeddingRequest{

kvcache/cache.go

@@ -56,8 +56,9 @@ type Cache interface {
 
 	// StartForward is called before the start of the model's forward pass.
 	// For each token in the coming batch, there must be a corresponding
-	// entry in positions and seqs.
-	StartForward(ctx ml.Context, batch input.Batch) error
+	// entry in positions and seqs. reserve is to preallocate memory
+	// without actually storing data in the cache.
+	StartForward(ctx ml.Context, batch input.Batch, reserve bool) error
 
 	// CopyPrefix copies tokens in the range [0, len) from srcSeq to dstSeq
 	CopyPrefix(srcSeq, dstSeq int, len int32)

kvcache/causal.go

@@ -146,51 +146,60 @@ func (c *Causal) Close() {
 	}
 }
 
-func (c *Causal) StartForward(ctx ml.Context, batch input.Batch) error {
+func (c *Causal) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
 	c.curBatchSize = len(batch.Positions)
 	c.curSequences = batch.Sequences
 	c.curPositions = batch.Positions
 	c.opts.Except = nil
 
-	c.updateSlidingWindow()
+	if !reserve {
+		c.updateSlidingWindow()
+
+		var err error
+		c.curLoc, err = c.findStartLoc()
+		if errors.Is(err, ErrKvCacheFull) {
+			c.defrag()
+			c.curLoc, err = c.findStartLoc()
+		}
+		if err != nil {
+			return err
+		}
+
+		c.curCellRange = newRange()
+		for i, pos := range batch.Positions {
+			seq := batch.Sequences[i]
+
+			c.cells[c.curLoc+i] = cacheCell{pos: pos, sequences: []int{seq}}
+
+			seqRange, ok := c.cellRanges[seq]
+			if !ok {
+				seqRange = newRange()
+			}
+
+			if c.curLoc+i > seqRange.max {
+				seqRange.max = c.curLoc + i
+			}
+			if seqRange.max > c.curCellRange.max {
+				c.curCellRange.max = seqRange.max
+			}
+
+			if c.curLoc+i < seqRange.min {
+				seqRange.min = c.curLoc + i
+			}
+			if seqRange.min < c.curCellRange.min {
+				c.curCellRange.min = seqRange.min
+			}
+			c.cellRanges[seq] = seqRange
+		}
+	} else {
+		// If we are reserving memory, don't update any of the cache metadata but set the size
+		// to the worst case.
+		c.curLoc = 0
+		c.curCellRange.min = 0
+		c.curCellRange.max = len(c.cells) - 1
+	}
 
 	var err error
-	c.curLoc, err = c.findStartLoc()
-	if errors.Is(err, ErrKvCacheFull) {
-		c.defrag()
-		c.curLoc, err = c.findStartLoc()
-	}
-	if err != nil {
-		return err
-	}
-
-	c.curCellRange = newRange()
-	for i, pos := range batch.Positions {
-		seq := batch.Sequences[i]
-
-		c.cells[c.curLoc+i] = cacheCell{pos: pos, sequences: []int{seq}}
-
-		seqRange, ok := c.cellRanges[seq]
-		if !ok {
-			seqRange = newRange()
-		}
-
-		if c.curLoc+i > seqRange.max {
-			seqRange.max = c.curLoc + i
-		}
-		if seqRange.max > c.curCellRange.max {
-			c.curCellRange.max = seqRange.max
-		}
-
-		if c.curLoc+i < seqRange.min {
-			seqRange.min = c.curLoc + i
-		}
-		if seqRange.min < c.curCellRange.min {
-			c.curCellRange.min = seqRange.min
-		}
-		c.cellRanges[seq] = seqRange
-	}
-
 	c.curMask, err = c.buildMask(ctx)
 
 	return err

kvcache/causal_test.go

@@ -5,7 +5,6 @@ import (
 	"slices"
 	"testing"
 
-	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model/input"
 )
@@ -281,7 +280,7 @@ func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase)
 			context := backend.NewContext()
 			defer context.Close()
 
-			err := cache.StartForward(context, input.Batch{Positions: test.pos, Sequences: test.seqs})
+			err := cache.StartForward(context, input.Batch{Positions: test.pos, Sequences: test.seqs}, false)
 			if err != nil {
 				panic(err)
 			}
@@ -315,7 +314,7 @@ func TestCanResume(t *testing.T) {
 	err := cache.StartForward(context, input.Batch{
 		Positions: []int32{0, 1, 2, 3},
 		Sequences: []int{0, 0, 0, 0},
-	})
+	}, false)
 	if err != nil {
 		t.Fatalf("StartForward failed: %v", err)
 	}
@@ -342,7 +341,7 @@ func TestCanResume(t *testing.T) {
 	err = cache.StartForward(context, input.Batch{
 		Positions: []int32{4, 5},
 		Sequences: []int{0, 0},
-	})
+	}, false)
 	if err != nil {
 		t.Fatalf("StartForward failed: %v", err)
 	}
@@ -372,14 +371,8 @@ func TestCanResume(t *testing.T) {
 	}
 }
 
-type testBackend struct{}
-
-func (b *testBackend) Config() fs.Config {
-	panic("not implemented")
-}
-
-func (b *testBackend) Get(name string) ml.Tensor {
-	panic("not implemented")
+type testBackend struct {
+	ml.Backend
 }
 
 func (b *testBackend) NewContext() ml.Context {
@@ -390,12 +383,10 @@ func (b *testBackend) NewContextSize(int) ml.Context {
 	return &testContext{}
 }
 
-func (b *testBackend) SystemInfo() string {
-	return "not implemented"
+type testContext struct {
+	ml.Context
 }
 
-type testContext struct{}
-
 func (c *testContext) Empty(dtype ml.DType, shape ...int) ml.Tensor {
 	total := 0
 
@@ -440,6 +431,8 @@ func (c *testContext) Forward(...ml.Tensor) ml.Context { return c }
 
 func (c *testContext) Compute(...ml.Tensor) {}
 
+func (c *testContext) Reserve() error { return nil }
+
 func (c *testContext) MaxGraphNodes() int {
 	return 10
 }
@@ -447,6 +440,8 @@ func (c *testContext) MaxGraphNodes() int {
 func (c *testContext) Close() {}
 
 type testTensor struct {
+	ml.Tensor
+
 	dtype       ml.DType
 	elementSize int
 	data        []float32
@@ -474,10 +469,6 @@ func (t *testTensor) DType() ml.DType {
 	return t.dtype
 }
 
-func (t *testTensor) Bytes() []byte {
-	panic("not implemented")
-}
-
 func (t *testTensor) Floats() []float32 {
 	out := make([]float32, len(t.data))
 	copy(out, t.data)
@@ -502,64 +493,6 @@ func (t *testTensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	return out
 }
 
-func (t *testTensor) Mul(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Mulmat(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) MulmatFullPrec(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Softmax(ctx ml.Context) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) LayerNorm(ctx ml.Context, weight, bias ml.Tensor, eps float32) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) RMSNorm(ctx ml.Context, weight ml.Tensor, eps float32) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Scale(ctx ml.Context, s float64) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) AvgPool1D(ctx ml.Context, k, s, p int) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Conv2D(ctx ml.Context, weight ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, dim, ropeType uint32, base, scale float32) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) IM2Col(ctx ml.Context, weight ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Cos(ctx ml.Context) ml.Tensor  { panic("not implemented") }
-func (t *testTensor) Sin(ctx ml.Context) ml.Tensor  { panic("not implemented") }
-func (t *testTensor) Tanh(ctx ml.Context) ml.Tensor { panic("not implemented") }
-func (t *testTensor) GELU(ctx ml.Context) ml.Tensor { panic("not implemented") }
-func (t *testTensor) SILU(ctx ml.Context) ml.Tensor { panic("not implemented") }
-
-func (t *testTensor) Reshape(ctx ml.Context, shape ...int) ml.Tensor {
-	panic("not implemented")
-}
-
 func (t *testTensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
 	offset /= t.elementSize
 
@@ -582,43 +515,7 @@ func (t *testTensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
 	return view
 }
 
-func (t *testTensor) Permute(ctx ml.Context, shape ...int) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Contiguous(ctx ml.Context) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Set(ctx ml.Context, t2 ml.Tensor, offset int, strides ...int) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Unpad(ctx ml.Context, shape ...int) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Stack(ctx ml.Context, dim int, s ...ml.Tensor) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Repeat(ctx ml.Context, dim, n int) ml.Tensor { panic("not implemented") }
-
-func (t *testTensor) Concat(ctx ml.Context, t2 ml.Tensor, dim int) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) Rows(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
-	panic("not implemented")
-}
-
 func (t *testTensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	copy(t2.(*testTensor).data, t.data)
 	return nil
 }
-
-func (t *testTensor) Duplicate(ctx ml.Context) ml.Tensor { panic("not implemented") }

kvcache/encoder.go

@@ -27,6 +27,11 @@ type EncoderCache struct {
 	// anything will be stored)
 	curPos int32
 
+	// curReserve indicates that this forward pass is only for
+	// memory reservation and we should not update our metadata
+	// based on it.
+	curReserve bool
+
 	// ** cache metadata **
 
 	// was something stored in the cache?
@@ -83,12 +88,14 @@ func (c *EncoderCache) Close() {
 	}
 }
 
-func (c *EncoderCache) StartForward(ctx ml.Context, batch input.Batch) error {
+func (c *EncoderCache) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
 	// We work with the most recent image
 	if len(batch.Multimodal) > 0 {
 		c.curPos = batch.Positions[batch.Multimodal[len(batch.Multimodal)-1].Index]
 	}
 
+	c.curReserve = reserve
+
 	return nil
 }
 
@@ -105,8 +112,10 @@ func (c *EncoderCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
 }
 
 func (c *EncoderCache) Put(ctx ml.Context, key, value ml.Tensor) {
-	c.encoderPos = c.curPos
-	c.encoderCached = true
+	if !c.curReserve {
+		c.encoderPos = c.curPos
+		c.encoderCached = true
+	}
 
 	if c.config.PermutedV {
 		value = value.Permute(ctx, 1, 2, 0, 3)

kvcache/wrapper.go

@@ -41,9 +41,9 @@ func (c *WrapperCache) Close() {
 	}
 }
 
-func (c *WrapperCache) StartForward(ctx ml.Context, batch input.Batch) error {
+func (c *WrapperCache) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
 	for i, cache := range c.caches {
-		err := cache.StartForward(ctx, batch)
+		err := cache.StartForward(ctx, batch, reserve)
 		if err != nil {
 			// unwind on error - Remove with endIndex set to math.MaxInt32 does not fail
 			for j := i - 1; j >= 0; j-- {

llm/memory.go

@@ -149,7 +149,7 @@ func EstimateGPULayers(gpus []discover.GpuInfo, f *ggml.GGML, projectors []strin
 	}
 
 	if graphPartialOffload == 0 {
-		graphPartialOffload = f.KV().GQA() * kvTotal / 6
+		graphPartialOffload = f.KV().GQAMax() * kvTotal / 6
 	}
 	if graphFullOffload == 0 {
 		graphFullOffload = graphPartialOffload

ml/backend.go

@@ -97,6 +97,13 @@ type Context interface {
 
 	Forward(...Tensor) Context
 	Compute(...Tensor)
+
+	// Reserve is analogous to Compute but rather than executing a
+	// graph, simply preallocates memory. Typically called with a
+	// worst case graph to ensure all resources are available for
+	// for future inference.
+	Reserve() error
+
 	MaxGraphNodes() int
 	Close()
 

ml/backend/ggml/ggml.go

@@ -10,6 +10,7 @@ import "C"
 
 import (
 	"context"
+	"errors"
 	"fmt"
 	"io"
 	"log/slog"
@@ -42,8 +43,12 @@ func devices() []*C.struct_ggml_backend_device {
 }
 
 type Backend struct {
-	meta    *fsggml.GGML
-	sched   *C.struct_ggml_backend_sched
+	meta *fsggml.GGML
+
+	sched         *C.struct_ggml_backend_sched
+	schedBackends []*C.struct_ggml_backend
+	schedBufts    []*C.struct_ggml_backend_buffer_type
+
 	tensors map[string]*C.struct_ggml_tensor
 
 	// input is the backend used for inputs
@@ -281,6 +286,10 @@ func New(ctx context.Context, r *os.File, params ml.BackendParams) (ml.Backend,
 		}
 
 		b := C.ggml_backend_alloc_ctx_tensors_from_buft(c, bt)
+		if b == nil {
+			return nil, fmt.Errorf("unable to allocate memory from device %v for model weights", C.GoString(C.ggml_backend_buft_name(bt)))
+		}
+
 		C.ggml_backend_buffer_set_usage(b, C.GGML_BACKEND_BUFFER_USAGE_WEIGHTS)
 		bbs[c] = b
 	}
@@ -385,8 +394,6 @@ func New(ctx context.Context, r *os.File, params ml.BackendParams) (ml.Backend,
 		schedBackends = append(schedBackends, b)
 		schedBufts = append(schedBufts, bt)
 
-		slog.Info("compute graph", "backend", C.GoString(C.ggml_backend_name(b)), "buffer_type", C.GoString(C.ggml_backend_buft_name(bt)))
-
 		if C.ggml_backend_is_cpu(b) {
 			// set number of threads for cpu backend
 			C.ggml_backend_cpu_set_n_threads(b, C.int(Threads(params.NumThreads)))
@@ -405,7 +412,9 @@ func New(ctx context.Context, r *os.File, params ml.BackendParams) (ml.Backend,
 			C.size_t(maxGraphNodes),
 			C._Bool(len(gpus) > 1 && slices.Contains(gpus, output.d)),
 		),
-		input: deviceBufferTypes[input.d],
+		schedBackends: schedBackends,
+		schedBufts:    schedBufts,
+		input:         deviceBufferTypes[input.d],
 		layers: func() map[int]*C.struct_ggml_backend_buffer_type {
 			m := make(map[int]*C.struct_ggml_backend_buffer_type)
 			for i, layer := range layers {
@@ -530,6 +539,24 @@ func (c Context) Compute(tensors ...ml.Tensor) {
 	}
 }
 
+func (c Context) Reserve() error {
+	if !C.ggml_backend_sched_reserve(c.b.sched, c.graph) {
+		C.ggml_backend_sched_reset(c.b.sched)
+		return errors.New("failed to reserve graph")
+	}
+
+	slog.Debug("compute graph", "nodes", C.ggml_graph_n_nodes(c.graph), "splits", C.ggml_backend_sched_get_n_splits(c.b.sched))
+	for i := range c.b.schedBackends {
+		size := C.ggml_backend_sched_get_buffer_size(c.b.sched, c.b.schedBackends[i])
+		slog.Info("compute graph", "backend", C.GoString(C.ggml_backend_name(c.b.schedBackends[i])), "buffer_type", C.GoString(C.ggml_backend_buft_name(c.b.schedBufts[i])),
+			"size", format.HumanBytes2(uint64(size)))
+	}
+
+	C.ggml_backend_sched_reset(c.b.sched)
+
+	return nil
+}
+
 func (c Context) MaxGraphNodes() int {
 	return c.maxGraphNodes
 }
@@ -547,9 +574,9 @@ func pad(length, pad C.size_t) C.size_t {
 	return ((length + pad - 1) / pad) * pad
 }
 
-func (c Context) newTensor(dtype ml.DType, shape []int) ml.Tensor {
+func (c Context) newTensor(dtype ml.DType, shape []int) (ml.Tensor, error) {
 	if c.buft == nil {
-		panic("set Input, Output, or Layer before creating tensors")
+		panic("set Input or Layer before creating tensors")
 	}
 
 	var cdtype uint32
@@ -570,7 +597,7 @@ func (c Context) newTensor(dtype ml.DType, shape []int) ml.Tensor {
 
 	if len(shape) < 1 || shape[0] == 0 {
 		var shape C.int64_t = 0
-		return &Tensor{b: c.b, t: C.ggml_new_tensor(c.ctx, cdtype, 1, &shape)}
+		return &Tensor{b: c.b, t: C.ggml_new_tensor(c.ctx, cdtype, 1, &shape)}, nil
 	} else if len(shape) > 4 {
 		panic("unsupported number of dimensions")
 	}
@@ -584,16 +611,29 @@ func (c Context) newTensor(dtype ml.DType, shape []int) ml.Tensor {
 	t := C.ggml_new_tensor(c.ctx, cdtype, C.int(len(shape)), shapeToGGML(shape))
 	size := pad(C.ggml_backend_buft_get_alloc_size(c.buft, t), C.ggml_backend_buft_get_alignment(c.buft))
 	b := C.ggml_backend_buft_alloc_buffer(c.buft, size)
+	if b == nil {
+		return nil, fmt.Errorf("unable to allocate %v from device %v for new tensor", format.HumanBytes2(uint64(size)), C.GoString(C.ggml_backend_buft_name(c.buft)))
+	}
+
 	C.ggml_backend_tensor_alloc(b, t, C.ggml_backend_buffer_get_base(b))
-	return &Tensor{b: c.b, t: t}
+	return &Tensor{b: c.b, t: t}, nil
 }
 
 func (c Context) Empty(dtype ml.DType, shape ...int) ml.Tensor {
-	return c.newTensor(dtype, shape)
+	t, err := c.newTensor(dtype, shape)
+	if err != nil {
+		panic(err)
+	}
+
+	return t
 }
 
 func (c Context) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
-	t := c.newTensor(dtype, shape)
+	t, err := c.newTensor(dtype, shape)
+	if err != nil {
+		panic(err)
+	}
+
 	C.ggml_set_zero(t.(*Tensor).t)
 	return t
 }
@@ -621,7 +661,11 @@ func (c Context) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
 		return nil, err
 	}
 
-	t := c.newTensor(ml.DTypeF32, shape)
+	t, err := c.newTensor(ml.DTypeF32, shape)
+	if err != nil {
+		return nil, err
+	}
+
 	if len(s) > 0 {
 		C.ggml_backend_tensor_set(t.(*Tensor).t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
 	}
@@ -634,7 +678,11 @@ func (c Context) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
 		return nil, err
 	}
 
-	t := c.newTensor(ml.DTypeI32, shape)
+	t, err := c.newTensor(ml.DTypeI32, shape)
+	if err != nil {
+		return nil, err
+	}
+
 	if len(s) > 0 {
 		C.ggml_backend_tensor_set(t.(*Tensor).t, unsafe.Pointer(&s[0]), 0, C.ggml_nbytes(t.(*Tensor).t))
 	}

model/model.go

@@ -299,7 +299,7 @@ func Forward(ctx ml.Context, m Model, inputs []int32, batch input.Batch) (ml.Ten
 
 	cache := m.Config().Cache
 	if cache != nil {
-		err := cache.StartForward(ctx, batch)
+		err := cache.StartForward(ctx, batch, false)
 		if err != nil {
 			return nil, err
 		}

openai/openai_test.go

@@ -281,27 +281,31 @@ func TestChatMiddleware(t *testing.T) {
 							Description: "Get the current weather",
 							Parameters: struct {
 								Type       string   `json:"type"`
+								Defs       any      `json:"$defs,omitempty"`
+								Items      any      `json:"items,omitempty"`
 								Required   []string `json:"required"`
 								Properties map[string]struct {
-									Type        string   `json:"type"`
-									Description string   `json:"description"`
-									Enum        []string `json:"enum,omitempty"`
+									Type        api.PropertyType `json:"type"`
+									Items       any              `json:"items,omitempty"`
+									Description string           `json:"description"`
+									Enum        []any            `json:"enum,omitempty"`
 								} `json:"properties"`
 							}{
 								Type:     "object",
 								Required: []string{"location"},
 								Properties: map[string]struct {
-									Type        string   `json:"type"`
-									Description string   `json:"description"`
-									Enum        []string `json:"enum,omitempty"`
+									Type        api.PropertyType `json:"type"`
+									Items       any              `json:"items,omitempty"`
+									Description string           `json:"description"`
+									Enum        []any            `json:"enum,omitempty"`
 								}{
 									"location": {
-										Type:        "string",
+										Type:        api.PropertyType{"string"},
 										Description: "The city and state",
 									},
 									"unit": {
-										Type: "string",
-										Enum: []string{"celsius", "fahrenheit"},
+										Type: api.PropertyType{"string"},
+										Enum: []any{"celsius", "fahrenheit"},
 									},
 								},
 							},

parser/parser.go

@@ -11,10 +11,13 @@ import (
 	"os"
 	"os/user"
 	"path/filepath"
+	"runtime"
 	"slices"
 	"strconv"
 	"strings"
+	"sync"
 
+	"golang.org/x/sync/errgroup"
 	"golang.org/x/text/encoding/unicode"
 	"golang.org/x/text/transform"
 
@@ -144,12 +147,25 @@ func fileDigestMap(path string) (map[string]string, error) {
 		files = []string{path}
 	}
 
+	var mu sync.Mutex
+	var g errgroup.Group
+	g.SetLimit(max(runtime.GOMAXPROCS(0)-1, 1))
 	for _, f := range files {
-		digest, err := digestForFile(f)
-		if err != nil {
-			return nil, err
-		}
-		fl[f] = digest
+		g.Go(func() error {
+			digest, err := digestForFile(f)
+			if err != nil {
+				return err
+			}
+
+			mu.Lock()
+			defer mu.Unlock()
+			fl[f] = digest
+			return nil
+		})
+	}
+
+	if err := g.Wait(); err != nil {
+		return nil, err
 	}
 
 	return fl, nil

runner/ollamarunner/cache_test.go

@@ -448,7 +448,7 @@ func (m *mockCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor)
 func (m *mockCache) Put(ctx ml.Context, key, value ml.Tensor)                                      {}
 func (m *mockCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {}
 func (m *mockCache) Close()                                                                        {}
-func (m *mockCache) StartForward(ctx ml.Context, batch input.Batch) error                          { return nil }
+func (m *mockCache) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error            { return nil }
 func (m *mockCache) CopyPrefix(srcSeq, dstSeq int, len int32)                                      {}
 func (m *mockCache) SetConfig(ml.CacheConfig)                                                      {}
 func (m *mockCache) CanResume(seq int, pos int32) bool                                             { return true }

runner/ollamarunner/runner.go

@@ -728,6 +728,51 @@ func (m *multiLPath) String() string {
 	return strings.Join(*m, ", ")
 }
 
+func (s *Server) reserveWorstCaseGraph() error {
+	ctx := s.model.Backend().NewContext()
+	defer ctx.Close()
+
+	var batch input.Batch
+
+	inputs := make([]int32, s.batchSize)
+	batch.Positions = make([]int32, len(inputs))
+	batch.Sequences = make([]int, len(inputs))
+	for i := range inputs {
+		batch.Positions[i] = int32(i)
+	}
+
+	batch.Outputs = make([]int32, s.parallel)
+	for i := range batch.Outputs {
+		batch.Outputs[i] = int32(i)
+	}
+
+	var err error
+	batch.Inputs, err = ctx.Input().FromIntSlice(inputs, len(inputs))
+	if err != nil {
+		return err
+	}
+
+	cache := s.model.Config().Cache
+	if cache != nil {
+		err := cache.StartForward(ctx, batch, true)
+		if err != nil {
+			return err
+		}
+	}
+
+	t, err := s.model.Forward(ctx, batch)
+	if err != nil {
+		return err
+	}
+
+	err = ctx.Forward(t).Reserve()
+	if err != nil {
+		return err
+	}
+
+	return nil
+}
+
 func (s *Server) loadModel(
 	ctx context.Context,
 	mpath string,
@@ -765,6 +810,11 @@ func (s *Server) loadModel(
 	s.seqs = make([]*Sequence, s.parallel)
 	s.seqsSem = semaphore.NewWeighted(int64(s.parallel))
 
+	err = s.reserveWorstCaseGraph()
+	if err != nil {
+		panic(err)
+	}
+
 	s.status = llm.ServerStatusReady
 	s.ready.Done()
 }

server/routes_generate_test.go

@@ -370,27 +370,31 @@ func TestGenerateChat(t *testing.T) {
 					Description: "Get the current weather",
 					Parameters: struct {
 						Type       string   `json:"type"`
+						Defs       any      `json:"$defs,omitempty"`
+						Items      any      `json:"items,omitempty"`
 						Required   []string `json:"required"`
 						Properties map[string]struct {
-							Type        string   `json:"type"`
-							Description string   `json:"description"`
-							Enum        []string `json:"enum,omitempty"`
+							Type        api.PropertyType `json:"type"`
+							Items       any              `json:"items,omitempty"`
+							Description string           `json:"description"`
+							Enum        []any            `json:"enum,omitempty"`
 						} `json:"properties"`
 					}{
 						Type:     "object",
 						Required: []string{"location"},
 						Properties: map[string]struct {
-							Type        string   `json:"type"`
-							Description string   `json:"description"`
-							Enum        []string `json:"enum,omitempty"`
+							Type        api.PropertyType `json:"type"`
+							Items       any              `json:"items,omitempty"`
+							Description string           `json:"description"`
+							Enum        []any            `json:"enum,omitempty"`
 						}{
 							"location": {
-								Type:        "string",
+								Type:        api.PropertyType{"string"},
 								Description: "The city and state",
 							},
 							"unit": {
-								Type: "string",
-								Enum: []string{"celsius", "fahrenheit"},
+								Type: api.PropertyType{"string"},
+								Enum: []any{"celsius", "fahrenheit"},
 							},
 						},
 					},
@@ -467,27 +471,31 @@ func TestGenerateChat(t *testing.T) {
 					Description: "Get the current weather",
 					Parameters: struct {
 						Type       string   `json:"type"`
+						Defs       any      `json:"$defs,omitempty"`
+						Items      any      `json:"items,omitempty"`
 						Required   []string `json:"required"`
 						Properties map[string]struct {
-							Type        string   `json:"type"`
-							Description string   `json:"description"`
-							Enum        []string `json:"enum,omitempty"`
+							Type        api.PropertyType `json:"type"`
+							Items       any              `json:"items,omitempty"`
+							Description string           `json:"description"`
+							Enum        []any            `json:"enum,omitempty"`
 						} `json:"properties"`
 					}{
 						Type:     "object",
 						Required: []string{"location"},
 						Properties: map[string]struct {
-							Type        string   `json:"type"`
-							Description string   `json:"description"`
-							Enum        []string `json:"enum,omitempty"`
+							Type        api.PropertyType `json:"type"`
+							Items       any              `json:"items,omitempty"`
+							Description string           `json:"description"`
+							Enum        []any            `json:"enum,omitempty"`
 						}{
 							"location": {
-								Type:        "string",
+								Type:        api.PropertyType{"string"},
 								Description: "The city and state",
 							},
 							"unit": {
-								Type: "string",
-								Enum: []string{"celsius", "fahrenheit"},
+								Type: api.PropertyType{"string"},
+								Enum: []any{"celsius", "fahrenheit"},
 							},
 						},
 					},

server/sched.go

@@ -667,13 +667,19 @@ func (runner *runnerRef) waitForVRAMRecovery() chan any {
 	return finished
 }
 
-type ByDuration []*runnerRef
+type ByDurationAndName []*runnerRef
 
-func (a ByDuration) Len() int      { return len(a) }
-func (a ByDuration) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
-func (a ByDuration) Less(i, j int) bool {
-	// uint64 to turn negative time (never unload) to largest
-	return uint64(a[i].sessionDuration) < uint64(a[j].sessionDuration)
+func (a ByDurationAndName) Len() int      { return len(a) }
+func (a ByDurationAndName) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
+func (a ByDurationAndName) Less(i, j int) bool {
+	// Primary sort by session duration (uint64 to handle negatives)
+	d1 := uint64(a[i].sessionDuration)
+	d2 := uint64(a[j].sessionDuration)
+	if d1 != d2 {
+		return d1 < d2
+	}
+	// Secondary sort by model path lex order
+	return a[i].modelPath < a[j].modelPath
 }
 
 // TODO - future consideration to pick runners based on size
@@ -775,7 +781,7 @@ func (s *Scheduler) findRunnerToUnload() *runnerRef {
 
 	// 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?
-	sort.Sort(ByDuration(runnerList))
+	sort.Sort(ByDurationAndName(runnerList))
 
 	// First try to find a runner that's already idle
 	for _, runner := range runnerList {