mlxrunner: batch the sampler across multiple sequences

Register sequences with Add/Remove; each Sample call takes any subset of
registered slots and samples one token per row, appending to each slot's
ring-buffer history. When all slots share Options and penalty rings are
full, one fused transform pass runs over the whole batch via a persistent
pooled history tensor; otherwise calls fall back to per-slot serial
processing indexed against the same pool.

Performance is unchanged for a single sequence, which is all that is
exposed for now.

app/ui/app/src/hooks/useChats.ts

@@ -381,7 +381,7 @@ export const useSendMessage = (chatId: string) => {
                     role: "assistant",
                     content: "",
                     thinking: "",
-                    model: effectiveModel,
+                    model: effectiveModel.model,
                   }),
                 );
                 lastMessage = newMessages[newMessages.length - 1];
@@ -433,7 +433,7 @@ export const useSendMessage = (chatId: string) => {
                     role: "assistant",
                     content: "",
                     thinking: "",
-                    model: effectiveModel,
+                    model: effectiveModel.model,
                   }),
                 );
                 lastMessage = newMessages[newMessages.length - 1];
@@ -520,7 +520,7 @@ export const useSendMessage = (chatId: string) => {
                     thinkingTimeStart:
                       lastMessage.thinkingTimeStart || event.thinkingTimeStart,
                     thinkingTimeEnd: event.thinkingTimeEnd,
-                    model: selectedModel,
+                    model: selectedModel.model,
                   });
                   newMessages[newMessages.length - 1] = updatedMessage;
                 } else {
@@ -533,7 +533,7 @@ export const useSendMessage = (chatId: string) => {
                       tool_calls: event.toolCalls,
                       thinkingTimeStart: event.thinkingTimeStart,
                       thinkingTimeEnd: event.thinkingTimeEnd,
-                      model: selectedModel,
+                      model: selectedModel.model,
                     }),
                   );
                 }
@@ -699,7 +699,7 @@ export const useSendMessage = (chatId: string) => {
             queryClient.setQueryData(["chat", newId], {
               chat: new Chat({
                 id: newId,
-                model: effectiveModel,
+                model: effectiveModel.model,
                 messages: [
                   new Message({
                     role: "user",

cmd/cmd.go

@@ -1975,8 +1975,61 @@ func launchInteractiveModel(cmd *cobra.Command, modelName string) error {
 		Options:     map[string]any{},
 		ShowConnect: true,
 	}
-	// loadOrUnloadModel is cloud-safe here: remote/cloud models skip local preload
-	// and only validate auth/connectivity before interactive chat starts.
+
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	requestedCloud := modelref.HasExplicitCloudSource(modelName)
+
+	info, err := func() (*api.ShowResponse, error) {
+		showReq := &api.ShowRequest{Name: modelName}
+		info, err := client.Show(cmd.Context(), showReq)
+		var se api.StatusError
+		if errors.As(err, &se) && se.StatusCode == http.StatusNotFound {
+			if requestedCloud {
+				return nil, err
+			}
+			if err := PullHandler(cmd, []string{modelName}); err != nil {
+				return nil, err
+			}
+			return client.Show(cmd.Context(), &api.ShowRequest{Name: modelName})
+		}
+		return info, err
+	}()
+	if err != nil {
+		if handleCloudAuthorizationError(err) {
+			return nil
+		}
+		return err
+	}
+
+	ensureCloudStub(cmd.Context(), client, modelName)
+
+	opts.Think, err = inferThinkingOption(&info.Capabilities, &opts, false)
+	if err != nil {
+		return err
+	}
+
+	audioCapable := slices.Contains(info.Capabilities, model.CapabilityAudio)
+	opts.MultiModal = slices.Contains(info.Capabilities, model.CapabilityVision) || audioCapable
+
+	// TODO: remove the projector info and vision info checks below,
+	// these are left in for backwards compatibility with older servers
+	// that don't have the capabilities field in the model info
+	if len(info.ProjectorInfo) != 0 {
+		opts.MultiModal = true
+	}
+	for k := range info.ModelInfo {
+		if strings.Contains(k, ".vision.") {
+			opts.MultiModal = true
+			break
+		}
+	}
+
+	applyShowResponseToRunOptions(&opts, info)
+
 	if err := loadOrUnloadModel(cmd, &opts); err != nil {
 		return fmt.Errorf("error loading model: %w", err)
 	}

x/mlxrunner/client.go

@@ -226,7 +226,7 @@ func (c *Client) Completion(ctx context.Context, req llm.CompletionRequest, fn f
 
 	if resp.StatusCode != http.StatusOK {
 		respBody, _ := io.ReadAll(resp.Body)
-		return fmt.Errorf("%s", strings.TrimSpace(string(respBody)))
+		return api.StatusError{StatusCode: resp.StatusCode, ErrorMessage: strings.TrimSpace(string(respBody))}
 	}
 
 	scanner := bufio.NewScanner(resp.Body)

x/mlxrunner/mlx/array.go

@@ -10,6 +10,8 @@ import (
 	"reflect"
 	"sort"
 	"strings"
+	"sync"
+	"sync/atomic"
 	"unsafe"
 
 	"github.com/ollama/ollama/logutil"
@@ -18,20 +20,28 @@ import (
 type Array struct {
 	ctx    C.mlx_array
 	name   string
-	pinned int
+	pinned atomic.Int32
 }
 
-var arrays []*Array
+var (
+	arrays   []*Array
+	arraysMu sync.Mutex
+)
 
 // constructor utilities
 
 func New(name string) *Array {
 	t := &Array{name: name}
+
 	if tracing {
 		traceScratch = append(traceScratch, t)
 	} else {
+		arraysMu.Lock()
+		defer arraysMu.Unlock()
+
 		arrays = append(arrays, t)
 	}
+
 	return t
 }
 
@@ -131,7 +141,7 @@ func (t *Array) Clone() *Array {
 func Pin(s ...*Array) {
 	for _, t := range s {
 		if t != nil {
-			t.pinned++
+			t.pinned.Add(1)
 		}
 	}
 }
@@ -140,8 +150,7 @@ func Pin(s ...*Array) {
 func Unpin(s ...*Array) {
 	for _, t := range s {
 		if t != nil {
-			t.pinned--
-			if t.pinned < 0 {
+			if t.pinned.Add(-1) < 0 {
 				panic(fmt.Sprintf("mlx.Unpin: negative pin count on array %q", t.name))
 			}
 		}
@@ -151,9 +160,11 @@ func Unpin(s ...*Array) {
 // Sweep releases all unpinned arrays, primarily intermediate tensors. MLX will truly
 // free them when there are no other references, including dependencies in the graph.
 func Sweep() {
+	arraysMu.Lock()
+	defer arraysMu.Unlock()
 	n := 0
 	for _, t := range arrays {
-		if t.pinned > 0 && t.Valid() {
+		if t.pinned.Load() > 0 && t.Valid() {
 			arrays[n] = t
 			n++
 		} else if t.Valid() {
@@ -180,7 +191,7 @@ func (t *Array) String() string {
 func (t *Array) LogValue() slog.Value {
 	attrs := []slog.Attr{
 		slog.String("name", t.name),
-		slog.Int("pinned", t.pinned),
+		slog.Int("pinned", int(t.pinned.Load())),
 	}
 	if t.Valid() {
 		attrs = append(attrs,
@@ -194,19 +205,19 @@ func (t *Array) LogValue() slog.Value {
 
 // shape utilities
 
-func (t Array) Size() int {
+func (t *Array) Size() int {
 	return int(C.mlx_array_size(t.ctx))
 }
 
-func (t Array) NumBytes() int {
+func (t *Array) NumBytes() int {
 	return int(C.mlx_array_nbytes(t.ctx))
 }
 
-func (t Array) NumDims() int {
+func (t *Array) NumDims() int {
 	return int(C.mlx_array_ndim(t.ctx))
 }
 
-func (t Array) Dims() []int {
+func (t *Array) Dims() []int {
 	dims := make([]int, t.NumDims())
 	for i := range dims {
 		dims[i] = t.Dim(i)
@@ -215,29 +226,29 @@ func (t Array) Dims() []int {
 	return dims
 }
 
-func (t Array) Dim(dim int) int {
+func (t *Array) Dim(dim int) int {
 	return int(C.mlx_array_dim(t.ctx, C.int(dim)))
 }
 
-func (t Array) DType() DType {
+func (t *Array) DType() DType {
 	return DType(C.mlx_array_dtype(t.ctx))
 }
 
 // data utilities
 
-func (t Array) Int() int {
+func (t *Array) Int() int {
 	var item C.int64_t
 	C.mlx_array_item_int64(&item, t.ctx)
 	return int(item)
 }
 
-func (t Array) Float() float64 {
+func (t *Array) Float() float64 {
 	var item C.double
 	C.mlx_array_item_float64(&item, t.ctx)
 	return float64(item)
 }
 
-func (t Array) Ints() []int {
+func (t *Array) Ints() []int {
 	if dt := t.DType(); dt != DTypeInt32 {
 		panic(fmt.Sprintf("mlx: Ints requires DTypeInt32, got %v", dt))
 	}
@@ -248,7 +259,7 @@ func (t Array) Ints() []int {
 	return ints
 }
 
-func (t Array) Floats() []float32 {
+func (t *Array) Floats() []float32 {
 	if dt := t.DType(); dt != DTypeFloat32 {
 		panic(fmt.Sprintf("mlx: Floats requires DTypeFloat32, got %v", dt))
 	}
@@ -259,7 +270,7 @@ func (t Array) Floats() []float32 {
 	return floats
 }
 
-func (t Array) Save(name string) error {
+func (t *Array) Save(name string) error {
 	cName := C.CString(name)
 	defer C.free(unsafe.Pointer(cName))
 	C.mlx_save(cName, t.ctx)
@@ -268,6 +279,8 @@ func (t Array) Save(name string) error {
 
 // LogArrays logs all live arrays, sorted by size
 func LogArrays() {
+	arraysMu.Lock()
+	defer arraysMu.Unlock()
 	sort.Slice(arrays, func(i, j int) bool {
 		return arrays[i].NumBytes() > arrays[j].NumBytes()
 	})
@@ -276,7 +289,7 @@ func LogArrays() {
 	for _, t := range arrays {
 		nb := t.NumBytes()
 		total += nb
-		logutil.Trace(fmt.Sprintf("tensor %-60s %5s %5s pinned=%d %v", t.name, t.DType(), PrettyBytes(nb), t.pinned, t.Dims()))
+		logutil.Trace(fmt.Sprintf("tensor %-60s %5s %5s pinned=%d %v", t.name, t.DType(), PrettyBytes(nb), t.pinned.Load(), t.Dims()))
 	}
 	logutil.Trace(fmt.Sprintf("tensors total: %d, size: %s, active: %s", len(arrays), PrettyBytes(total), PrettyBytes(ActiveMemory())))
 }

x/mlxrunner/mlx/compile.go

@@ -150,7 +150,7 @@ func closureCallback(res *C.mlx_vector_array, input C.mlx_vector_array, payload
 	traceScratch = nil
 	defer func() {
 		for _, a := range traceScratch {
-			if a.pinned > 0 {
+			if a.pinned.Load() > 0 {
 				panic("mlx: traced array was pinned during compilation")
 			}
 			if a.Valid() {

x/mlxrunner/mlx/fast.go

@@ -24,8 +24,8 @@ func ScaledDotProductAttention(query, key, value, mask *Array, scale float32) *A
 }
 
 type LayerNorm struct {
-	Weight Array `weight:"weight"`
-	Bias   Array `weight:"bias"`
+	Weight *Array `weight:"weight"`
+	Bias   *Array `weight:"bias"`
 }
 
 func (r *LayerNorm) Forward(x *Array, eps float32) *Array {
@@ -35,10 +35,10 @@ func (r *LayerNorm) Forward(x *Array, eps float32) *Array {
 }
 
 type RMSNorm struct {
-	Weight Array `weight:"weight"`
+	Weight *Array `weight:"weight"`
 }
 
-func (r RMSNorm) Forward(x *Array, eps float32) *Array {
+func (r *RMSNorm) Forward(x *Array, eps float32) *Array {
 	out := New("FAST_RMSNORM")
 	C.mlx_fast_rms_norm(&out.ctx, x.ctx, r.Weight.ctx, C.float(eps), DefaultStream().ctx)
 	return out

x/mlxrunner/mlx/nn.go

@@ -1,12 +1,12 @@
 package mlx
 
 type Linear struct {
-	Weight Array `weight:"weight"`
-	Bias   Array `weight:"bias"`
+	Weight *Array `weight:"weight"`
+	Bias   *Array `weight:"bias"`
 }
 
 // Forward computes the linear transformation: x @ Weight.T + Bias
-func (m Linear) Forward(x *Array) *Array {
+func (m *Linear) Forward(x *Array) *Array {
 	w := m.Weight.Transpose(1, 0)
 	if m.Bias.Valid() {
 		return m.Bias.Addmm(x, w, 1.0, 1.0)
@@ -15,14 +15,14 @@ func (m Linear) Forward(x *Array) *Array {
 	return x.Matmul(w)
 }
 
-func (m Linear) Gather(x, lhs, rhs *Array, sorted bool) *Array {
+func (m *Linear) Gather(x, lhs, rhs *Array, sorted bool) *Array {
 	w := m.Weight.Transpose(0, 2, 1)
 	// TODO: bias
 	return x.GatherMM(w, lhs, rhs, sorted)
 }
 
 type Embedding struct {
-	Weight Array `weight:"weight"`
+	Weight *Array `weight:"weight"`
 }
 
 func (e *Embedding) Forward(indices *Array) *Array {

x/mlxrunner/mlx/ops.go

@@ -72,6 +72,10 @@ func (t *Array) AsStrided(shape []int, strides []int, offset int) *Array {
 }
 
 func (t *Array) Concatenate(axis int, others ...*Array) *Array {
+	if len(others) == 0 {
+		return t
+	}
+
 	vector := C.mlx_vector_array_new()
 	defer C.mlx_vector_array_free(vector)
 
@@ -127,9 +131,9 @@ func (t *Array) GatherMM(other, lhs, rhs *Array, sorted bool) *Array {
 	return out
 }
 
-func (t *Array) Logsumexp(keepDims bool) *Array {
-	out := New("LOGSUMEXP")
-	C.mlx_logsumexp(&out.ctx, t.ctx, C.bool(keepDims), DefaultStream().ctx)
+func (t *Array) LogsumexpAxis(axis int, keepDims bool) *Array {
+	out := New("LOGSUMEXP_AXIS")
+	C.mlx_logsumexp_axis(&out.ctx, t.ctx, C.int(axis), C.bool(keepDims), DefaultStream().ctx)
 	return out
 }
 

x/mlxrunner/mlx/ops_extra.go

@@ -376,6 +376,9 @@ func Concatenate(arrays []*Array, axis int) *Array {
 	if len(arrays) == 0 {
 		return nil
 	}
+	if len(arrays) == 1 {
+		return arrays[0]
+	}
 	return arrays[0].Concatenate(axis, arrays[1:]...)
 }
 

x/mlxrunner/pipeline.go

@@ -6,11 +6,9 @@ import (
 	"errors"
 	"fmt"
 	"log/slog"
-	"net/http"
 	"sort"
 	"time"
 
-	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
@@ -22,19 +20,44 @@ func prefillChunkSize() int {
 	return 2 << 10
 }
 
-func (r *Runner) TextGenerationPipeline(request Request) error {
+// Prepare tokenizes the prompt and validates it against the model's
+// context length. It is safe to call from any goroutine. On success it
+// populates request.Tokens and adjusts request.Options.NumPredict.
+func (r *Runner) Prepare(request *Request) error {
 	if r.Model == nil {
 		return errors.New("model not loaded")
 	}
 
+	tokens := r.Tokenizer.Encode(request.Prompt, r.Tokenizer.AddBOS())
+	if len(tokens) == 0 {
+		return errors.New("empty prompt")
+	}
+
+	if len(tokens) >= r.contextLength {
+		return fmt.Errorf("input length (%d tokens) exceeds the model's maximum context length (%d tokens)", len(tokens), r.contextLength)
+	}
+
+	// Cap generation to stay within the model's context length
+	maxGenerate := r.contextLength - len(tokens)
+	if request.Options.NumPredict <= 0 {
+		request.Options.NumPredict = maxGenerate
+	} else {
+		request.Options.NumPredict = min(request.Options.NumPredict, maxGenerate)
+	}
+
+	request.Tokens = tokens
+	return nil
+}
+
+// The runner serializes requests today so we just use a fixed slot ID.
+const pipelineSlot = 0
+
+func (r *Runner) TextGenerationPipeline(ctx context.Context, request Request) error {
 	mlx.ResetPeakMemory()
-	ctx := request.Ctx
 	var sample, nextSample sampler.Result
 
 	defer func() {
-		if request.Sampler != nil {
-			request.Sampler.Free()
-		}
+		r.Sampler.Remove(pipelineSlot)
 		mlx.Unpin(sample.Arrays()...)
 		mlx.Unpin(nextSample.Arrays()...)
 		mlx.Sweep()
@@ -47,27 +70,7 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 		slog.Info("peak memory", "size", mlx.PrettyBytes(mlx.PeakMemory()))
 	}()
 
-	inputs := r.Tokenizer.Encode(request.Prompt, r.Tokenizer.AddBOS())
-	if len(inputs) == 0 {
-		return errors.New("empty prompt")
-	}
-
-	if len(inputs) >= r.contextLength {
-		return api.StatusError{
-			StatusCode:   http.StatusBadRequest,
-			ErrorMessage: fmt.Sprintf("input length (%d tokens) exceeds the model's maximum context length (%d tokens)", len(inputs), r.contextLength),
-		}
-	}
-
-	// Cap generation to stay within the model's context length
-	maxGenerate := r.contextLength - len(inputs)
-	if request.Options.NumPredict <= 0 {
-		request.Options.NumPredict = maxGenerate
-	} else {
-		request.Options.NumPredict = min(request.Options.NumPredict, maxGenerate)
-	}
-
-	request.Sampler.ResetHistory(inputs)
+	inputs := request.Tokens
 
 	session := r.cache.begin(r.Model, inputs)
 	defer session.close()
@@ -119,7 +122,7 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 			}
 		}
 
-		r.Model.Forward(mlx.FromValues(tokens[processed:processed+n], n).ExpandDims(0), caches)
+		r.Model.Forward(mlx.FromValues(tokens[processed:processed+n], 1, n), caches)
 		mlx.Sweep()
 		materializeCaches()
 		processed += n
@@ -136,21 +139,28 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 		mlx.ClearCache()
 	}
 
+	// Register the sampler after prefill completes.
+	r.Sampler.Add(pipelineSlot, request.SamplerOpts, inputs)
+
 	step := func(token *mlx.Array) sampler.Result {
-		fwd := r.Model.Forward(token.ExpandDims(0), caches)
+		fwd := r.Model.Forward(token, caches)
 		logits := r.Model.Unembed(fwd)
 		logits = logits.Slice(mlx.Slice(), mlx.Slice(logits.Dim(1)-1), mlx.Slice()).Squeeze(1)
 
-		sample := request.Sampler.Sample(logits)
+		sample := r.Sampler.Sample([]int{pipelineSlot}, logits)
 		mlx.Pin(sample.Arrays()...)
 		mlx.Sweep()
 		mlx.AsyncEval(sample.Arrays()...)
 		return sample
 	}
 
-	sample = step(mlx.FromValues(tokens[processed:], total-processed))
+	sample = step(mlx.FromValues(tokens[processed:], 1, total-processed))
 
-	dec := decoder{tokenizer: r.Tokenizer}
+	dec := decoder{
+		tokenizer:       r.Tokenizer,
+		wantLogprobs:    request.SamplerOpts.Logprobs,
+		wantTopLogprobs: request.SamplerOpts.TopLogprobs,
+	}
 
 	final := CompletionResponse{Done: true, PromptEvalCount: len(inputs), EvalCount: request.Options.NumPredict, DoneReason: 1}
 	for i := range request.Options.NumPredict {
@@ -158,8 +168,7 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 			return err
 		}
 
-		request.Sampler.AppendToken(sample.Token)
-		nextSample = step(sample.Token)
+		nextSample = step(sample.Token.ExpandDims(-1))
 
 		if i == 0 {
 			mlx.Eval(sample.Arrays()...)
@@ -206,15 +215,17 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 // with those bytes so Content and Logprobs stay aligned when a chunk does
 // flush.
 type decoder struct {
-	tokenizer *tokenizer.Tokenizer
-	buf       bytes.Buffer
-	logprobs  []llm.Logprob
+	tokenizer       *tokenizer.Tokenizer
+	buf             bytes.Buffer
+	logprobs        []llm.Logprob
+	wantLogprobs    bool
+	wantTopLogprobs int
 }
 
 func (d *decoder) decode(res sampler.Result) (CompletionResponse, bool) {
 	output := int32(res.Token.Int())
 	d.buf.WriteString(d.tokenizer.Decode([]int32{output}))
-	d.logprobs = append(d.logprobs, buildLogprob(res, d.tokenizer.Decode)...)
+	d.logprobs = append(d.logprobs, buildLogprob(res, d.wantLogprobs, d.wantTopLogprobs, d.tokenizer.Decode)...)
 
 	content := flushValidUTF8Prefix(&d.buf)
 	if content == "" {
@@ -225,8 +236,13 @@ func (d *decoder) decode(res sampler.Result) (CompletionResponse, bool) {
 	return resp, true
 }
 
-func buildLogprob(sample sampler.Result, decode func([]int32) string) []llm.Logprob {
-	if sample.Logprob == nil {
+// buildLogprob converts the sampler's logprob tensors into the wire-format
+// llm.Logprob entries the caller wants. The sampler populates its logprob
+// tensors whenever any registered slot requested them, so the caller must
+// gate emission on its own request config (wantLogprobs / wantTopLogprobs)
+// rather than on whether the tensors happen to be non-nil.
+func buildLogprob(sample sampler.Result, wantLogprobs bool, wantTopLogprobs int, decode func([]int32) string) []llm.Logprob {
+	if !wantLogprobs || sample.Logprob == nil {
 		return nil
 	}
 	tok := func(id int32) string { return decode([]int32{id}) }
@@ -238,7 +254,7 @@ func buildLogprob(sample sampler.Result, decode func([]int32) string) []llm.Logp
 		},
 	}
 
-	if sample.TopTokens != nil {
+	if wantTopLogprobs > 0 && sample.TopTokens != nil {
 		ids := sample.TopTokens.Ints()
 		vals := sample.TopLogprobs.Floats()
 		pairs := make([]llm.TokenLogprob, len(ids))
@@ -248,9 +264,14 @@ func buildLogprob(sample sampler.Result, decode func([]int32) string) []llm.Logp
 				Logprob: float64(vals[i]),
 			}
 		}
+		// The sampler emits the top maxK across registered slots via
+		// Argpartition, which leaves entries unsorted.
 		sort.Slice(pairs, func(i, j int) bool {
 			return pairs[i].Logprob > pairs[j].Logprob
 		})
+		if wantTopLogprobs < len(pairs) {
+			pairs = pairs[:wantTopLogprobs]
+		}
 		out.TopLogprobs = pairs
 	}
 	return []llm.Logprob{out}

x/mlxrunner/runner.go

@@ -18,20 +18,25 @@ import (
 	"github.com/ollama/ollama/x/tokenizer"
 )
 
+// Request is a short-lived struct that carries a completion request through
+// a channel from the HTTP handler to the runner goroutine. The ctx field
+// must travel with the request so that cancellation propagates across the
+// channel boundary.
 type Request struct {
 	CompletionRequest
 	Responses chan CompletionResponse
-	Pipeline  func(Request) error
+	Pipeline  func(context.Context, Request) error
 
-	Ctx context.Context
-
-	Sampler *sample.Sampler
+	Ctx         context.Context //nolint:containedctx
+	Tokens      []int32
+	SamplerOpts sample.Options
 }
 
 type Runner struct {
 	Model         base.Model
 	Tokenizer     *tokenizer.Tokenizer
 	Requests      chan Request
+	Sampler       *sample.Sampler
 	cache         kvCache
 	contextLength int
 }
@@ -63,6 +68,7 @@ func (r *Runner) Load(modelName string) error {
 	r.Model = m
 	r.Tokenizer = m.Tokenizer()
 	r.contextLength = m.MaxContextLength()
+	r.Sampler = sample.New(r.contextLength)
 
 	mlx.EnableCompile()
 	return nil
@@ -131,7 +137,7 @@ func (r *Runner) Run(host, port string, mux http.Handler) error {
 			case <-ctx.Done():
 				return nil
 			case request := <-r.Requests:
-				if err := request.Pipeline(request); err != nil {
+				if err := request.Pipeline(request.Ctx, request); err != nil {
 					slog.Info("Request terminated", "error", err)
 					var statusErr api.StatusError
 					if !errors.As(err, &statusErr) {

x/mlxrunner/sample/logprob_test.go

@@ -24,14 +24,15 @@ type logprobEntry struct {
 func runSampleLogprobs(t *testing.T, logits []float32, topK int) (int, float64, []logprobEntry) {
 	t.Helper()
 
-	s := New(Options{Logprobs: true, TopLogprobs: topK})
+	s := New(128)
 	defer func() {
 		s.Free()
 		mlx.Sweep()
 	}()
+	s.Add(0, Options{Logprobs: true, TopLogprobs: topK}, nil)
 
 	tensor := mlx.FromValues(logits, 1, len(logits))
-	res := s.Sample(tensor)
+	res := s.Sample([]int{0}, tensor)
 
 	mlx.Pin(res.Arrays()...)
 	defer mlx.Unpin(res.Arrays()...)
@@ -225,6 +226,42 @@ func TestSampleLogprobsSelectedTokenCorrectness(t *testing.T) {
 	}
 }
 
+// TestBatchedLogprobsPerRow verifies that per-row logprobs in a batched
+// sample call match the per-slot reference. The numerically-stable softmax
+// must reduce along the last axis only, not over the whole batch.
+func TestBatchedLogprobsPerRow(t *testing.T) {
+	rowA := []float32{2, 1, 0}
+	rowB := []float32{0, 5, 0}
+
+	_, wantA, _ := runSampleLogprobs(t, rowA, 0)
+	_, wantB, _ := runSampleLogprobs(t, rowB, 0)
+
+	s := New(128)
+	t.Cleanup(func() {
+		s.Free()
+		mlx.Sweep()
+	})
+	s.Add(1, Options{Logprobs: true}, nil)
+	s.Add(2, Options{Logprobs: true}, nil)
+
+	logits := mlx.FromValues(append(append([]float32{}, rowA...), rowB...), 2, 3)
+	res := s.Sample([]int{1, 2}, logits)
+	mlx.Pin(res.Arrays()...)
+	t.Cleanup(func() { mlx.Unpin(res.Arrays()...) })
+	mlx.Eval(res.Arrays()...)
+
+	got := res.Logprob.Floats()
+	if len(got) != 2 {
+		t.Fatalf("Logprob length = %d, want 2", len(got))
+	}
+	if math.Abs(float64(got[0])-wantA) > 1e-5 {
+		t.Errorf("row 0 logprob = %f, want %f (per-slot reference)", got[0], wantA)
+	}
+	if math.Abs(float64(got[1])-wantB) > 1e-5 {
+		t.Errorf("row 1 logprob = %f, want %f (per-slot reference)", got[1], wantB)
+	}
+}
+
 func TestSampleLogprobsTopKOrdering(t *testing.T) {
 	// Logits chosen so argmax order differs from index order.
 	logits := []float32{2.0, 5.0, 1.0, 4.0, 3.0}

x/mlxrunner/sample/sample.go

@@ -1,13 +1,13 @@
 package sample
 
 import (
+	"fmt"
 	"math"
+	"slices"
 
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
 )
 
-type Transform func(*Sampler, *mlx.Array) *mlx.Array
-
 type Options struct {
 	Temperature      float32
 	TopP             float32
@@ -24,21 +24,15 @@ type Options struct {
 	TopLogprobs int
 }
 
-type Sampler struct {
-	Options
-
-	history    *mlx.Array
-	historyLen int
-	transforms []Transform
-}
-
-// Result bundles the outputs of one decode step. The logprob tensors are
-// populated only when the sampler is configured to report them.
+// Result bundles the outputs of one decode step. Logprob/TopTokens/
+// TopLogprobs are populated whenever any registered slot has Logprobs
+// (respectively TopLogprobs>0). Consumers need to filter by their
+// per-slot Options.
 type Result struct {
-	Token       *mlx.Array // sampled token id, shape [B]
-	Logprob     *mlx.Array // sampled-token logprob, shape [B,1]; nil unless Logprobs
-	TopTokens   *mlx.Array // top-K token ids, shape [B,K]; nil unless TopLogprobs > 0
-	TopLogprobs *mlx.Array // top-K logprobs, shape [B,K]; nil unless TopLogprobs > 0
+	Token       *mlx.Array // sampled token ids,       shape [B]
+	Logprob     *mlx.Array // sampled-token logprobs,  shape [B,1];    nil unless any registered slot has Logprobs
+	TopTokens   *mlx.Array // top-K token ids,         shape [B,maxK]; nil unless any registered slot has TopLogprobs>0
+	TopLogprobs *mlx.Array // top-K logprobs,          shape [B,maxK]; same
 }
 
 // Arrays returns the tensor fields as a slice so callers can drive the mlx
@@ -48,121 +42,300 @@ func (r Result) Arrays() []*mlx.Array {
 	return []*mlx.Array{r.Token, r.Logprob, r.TopTokens, r.TopLogprobs}
 }
 
-func New(opts Options) *Sampler {
-	if opts.RepeatPenalty <= 0 {
-		opts.RepeatPenalty = 1
+// Sampler is a batched, slot-based sampler. Sequences are registered with
+// Add and released with Remove. Each Sample call takes a subset of
+// registered slots (in any order) with their [B,V] logits, samples one
+// token per row, and appends it to that slot's ring-buffer history. Slots
+// not named in a given call are untouched.
+type Sampler struct {
+	slots []*slotState
+	byID  map[int]*slotState
+
+	// history is the pooled ring-buffer storage, [B, W] int32. Row i
+	// belongs to slots[i]; W is max(RepeatLastN) across penalty slots.
+	// Allocated on the first penalty slot, rebuilt only in Add/Remove.
+	history *mlx.Array
+
+	// allSameOpts: every registered slot shares Options. When true the
+	// canonical shared value is s.slots[0].opts.
+	allSameOpts bool
+
+	// anyLogprobs / maxTopLogprobs: compute-for-all output config.
+	// Sample populates Logprob (and Top* when maxTopLogprobs>0) whenever
+	// any registered slot requests them, even if that slot isn't in the
+	// current call.
+	anyLogprobs    bool
+	maxTopLogprobs int
+
+	// numCtx is the runner's context window; normalize uses it to
+	// resolve the repeat_last_n == -1 sentinel.
+	numCtx int
+}
+
+type slotState struct {
+	opts       Options
+	transforms []transform
+	historyLen int
+}
+
+type slotCtx struct {
+	opts    Options
+	history *mlx.Array // 2D [B, W] when penalties are configured; nil otherwise
+}
+
+type transform func(*slotCtx, *mlx.Array) *mlx.Array
+
+// New constructs an empty sampler with no registered slots. numCtx is
+// the runner's context window and must be positive.
+func New(numCtx int) *Sampler {
+	return &Sampler{
+		byID:        make(map[int]*slotState),
+		allSameOpts: true,
+		numCtx:      numCtx,
+	}
+}
+
+// historyWidth returns the column count of the pooled history tensor,
+// or 0 when no penalty slot has forced it to be allocated.
+func (s *Sampler) historyWidth() int {
+	if s.history == nil {
+		return 0
+	}
+	return s.history.Dim(1)
+}
+
+func (o Options) usesHistory() bool {
+	// RepeatLastN == 0 disables the penalty ring per the repeat_last_n API
+	// contract (0 = disabled), overriding any penalty coefficients.
+	if o.RepeatLastN == 0 {
+		return false
+	}
+	return o.RepeatPenalty != 1 || o.PresencePenalty != 0 || o.FrequencyPenalty != 0
+}
+
+func (o Options) normalize(numCtx int) Options {
+	if o.RepeatPenalty <= 0 {
+		o.RepeatPenalty = 1
+	}
+	// Resolve the repeat_last_n == -1 sentinel ("-1 = num_ctx") against
+	// the caller's context window.
+	if o.RepeatLastN < 0 {
+		o.RepeatLastN = numCtx
+	}
+	if !o.usesHistory() {
+		// Zero the ring capacity so slots that differ only in a spurious
+		// RepeatLastN still batch together and don't inflate pool width.
+		o.RepeatLastN = 0
+	}
+	return o
+}
+
+func (o Options) buildTransforms() []transform {
+	var ts []transform
+	if o.usesHistory() {
+		ts = append(ts, penalty)
 	}
 
-	s := &Sampler{Options: opts}
-
-	var transforms []Transform
-	if s.usesHistory() {
-		transforms = append(transforms, penalty)
-	}
-
-	hasTopP := opts.TopP > 0 && opts.TopP < 1
-	hasTopK := opts.TopK > 0
+	hasTopP := o.TopP > 0 && o.TopP < 1
+	hasTopK := o.TopK > 0
 	switch {
 	case hasTopP:
 		// topKTopP always does a full descending sort for the top-P
 		// cumulative mask and opportunistically masks top-K during the
 		// same pass when it is also configured.
-		transforms = append(transforms, topKTopP)
+		ts = append(ts, topKTopP)
 	case hasTopK:
 		// Argpartition (partial sort) is cheaper than a full sort.
-		transforms = append(transforms, topK)
+		ts = append(ts, topK)
 	}
 
-	if opts.MinP != 0 {
-		transforms = append(transforms, minP)
+	if o.MinP != 0 {
+		ts = append(ts, minP)
 	}
 
-	if opts.Temperature == 0 {
-		transforms = append(transforms, greedy)
+	if o.Temperature == 0 {
+		ts = append(ts, greedy)
 	} else {
-		transforms = append(transforms, temperature)
+		ts = append(ts, temperature)
 	}
-
-	s.transforms = transforms
-	return s
+	return ts
 }
 
-func (s *Sampler) usesHistory() bool {
-	return s.RepeatPenalty != 1 || s.PresencePenalty != 0 || s.FrequencyPenalty != 0
-}
-
-func (s *Sampler) setHistory(history *mlx.Array, historyLen int) {
-	if history != nil {
-		mlx.Pin(history)
+// Add registers a sequence under seqID. The last RepeatLastN entries of
+// priorTokens seed the ring buffer.
+func (s *Sampler) Add(seqID int, opts Options, priorTokens []int32) {
+	if _, dup := s.byID[seqID]; dup {
+		panic(fmt.Sprintf("sample.Sampler.Add: seqID %d already registered", seqID))
 	}
-	if s.history != nil {
+
+	opts = opts.normalize(s.numCtx)
+	slot := &slotState{
+		opts:       opts,
+		transforms: opts.buildTransforms(),
+	}
+
+	// Grow the pool to hold this slot's row. The pool is lazy — the first
+	// penalty slot allocates it — and thereafter every registered slot
+	// gets a row (rows for non-penalty slots are zero and never read).
+	// Invariant: s.history is pinned whenever non-nil.
+	if s.history != nil || opts.usesHistory() {
+		targetWidth := max(opts.RepeatLastN, s.historyWidth())
+		newRow := makeHistoryRow(priorTokens, opts.RepeatLastN, targetWidth)
+
+		var pool *mlx.Array
+		switch {
+		case s.history == nil && len(s.slots) == 0:
+			pool = newRow
+		case s.history == nil:
+			// First penalty slot with non-penalty slots already registered;
+			// seed zero rows so s.slots and pool row indices stay aligned.
+			zeros := mlx.Zeros(mlx.DTypeInt32, len(s.slots), targetWidth)
+			pool = zeros.Concatenate(0, newRow)
+		case targetWidth > s.historyWidth():
+			pad := mlx.Zeros(mlx.DTypeInt32, s.history.Dim(0), targetWidth-s.historyWidth())
+			pool = s.history.Concatenate(1, pad).Concatenate(0, newRow)
+		default:
+			pool = s.history.Concatenate(0, newRow)
+		}
+
+		mlx.Pin(pool)
 		mlx.Unpin(s.history)
+		s.history = pool
+
+		if opts.usesHistory() {
+			// Cap on seed so the next write's ring position
+			// (historyLen % RepeatLastN) lands at 0, overwriting the
+			// oldest entry when the ring was filled from priors.
+			slot.historyLen = min(len(priorTokens), opts.RepeatLastN)
+		}
 	}
-	s.history = history
-	s.historyLen = historyLen
+
+	s.slots = append(s.slots, slot)
+	s.byID[seqID] = slot
+	s.recomputeInvariants()
 }
 
-func (s *Sampler) ResetHistory(history []int32) {
-	if !s.usesHistory() {
+// makeHistoryRow builds a [1, width] int32 row with the last repeatLastN
+// entries of priorTokens packed into [0, min(len, repeatLastN)), zeros
+// elsewhere.
+func makeHistoryRow(priorTokens []int32, repeatLastN, width int) *mlx.Array {
+	take := min(len(priorTokens), repeatLastN)
+	if take <= 0 {
+		return mlx.Zeros(mlx.DTypeInt32, 1, width)
+	}
+	row := make([]int32, width)
+	copy(row, priorTokens[len(priorTokens)-take:])
+	return mlx.NewArrayInt32(row, []int32{1, int32(width)})
+}
+
+// recomputeInvariants refreshes allSameOpts and anyLogprobs/maxTopLogprobs
+// from s.slots. Called at the end of Add and Remove.
+func (s *Sampler) recomputeInvariants() {
+	if len(s.slots) == 0 {
+		s.allSameOpts = true
+		s.anyLogprobs = false
+		s.maxTopLogprobs = 0
 		return
 	}
-	if s.RepeatLastN > 0 && len(history) > s.RepeatLastN {
-		history = history[len(history)-s.RepeatLastN:]
+	first := s.slots[0].opts
+	s.allSameOpts = true
+	s.anyLogprobs = false
+	s.maxTopLogprobs = 0
+	for _, slot := range s.slots {
+		if slot.opts != first {
+			s.allSameOpts = false
+		}
+		if slot.opts.Logprobs {
+			s.anyLogprobs = true
+			if slot.opts.TopLogprobs > s.maxTopLogprobs {
+				s.maxTopLogprobs = slot.opts.TopLogprobs
+			}
+		}
 	}
-	if len(history) == 0 {
-		s.setHistory(nil, 0)
+}
+
+// Remove releases the slot. The pool tensor is rebuilt to drop the row.
+func (s *Sampler) Remove(seqID int) {
+	slot, ok := s.byID[seqID]
+	if !ok {
+		return
+	}
+	delete(s.byID, seqID)
+
+	row := slices.Index(s.slots, slot)
+	s.slots = slices.Delete(s.slots, row, row+1)
+	s.recomputeInvariants()
+
+	if s.history == nil {
 		return
 	}
 
-	tokens := append([]int32(nil), history...)
-	s.setHistory(mlx.NewArrayInt32(tokens, []int32{int32(len(tokens))}), len(tokens))
-}
-
-func (s *Sampler) AppendToken(token *mlx.Array) {
-	if !s.usesHistory() || token == nil {
-		return
-	}
-
-	next := token.AsType(mlx.DTypeInt32)
-	nextLen := next.Size()
-
-	if s.history != nil && s.historyLen > 0 {
-		next = s.history.Concatenate(0, next)
-		nextLen += s.historyLen
-	}
-
-	if s.RepeatLastN > 0 && nextLen > s.RepeatLastN {
-		trim := nextLen - s.RepeatLastN
-		next = next.Slice(mlx.Slice(trim, nextLen))
-		nextLen = s.RepeatLastN
-	}
-
-	s.setHistory(next, nextLen)
+	n := s.history.Dim(0)
+	var newHistory *mlx.Array
+	switch {
+	case n == 1:
+		newHistory = nil
+	case row == 0:
+		newHistory = s.history.Slice(mlx.Slice(1, n), mlx.Slice())
+	case row == n-1:
+		newHistory = s.history.Slice(mlx.Slice(0, row), mlx.Slice())
+	default:
+		before := s.history.Slice(mlx.Slice(0, row), mlx.Slice())
+		after := s.history.Slice(mlx.Slice(row+1, n), mlx.Slice())
+		newHistory = before.Concatenate(0, after)
+	}
+
+	mlx.Pin(newHistory)
+	mlx.Unpin(s.history)
+	s.history = newHistory
 }
 
+// Free releases the pooled history tensor and resets the sampler to the
+// New-equivalent state so it may be reused.
 func (s *Sampler) Free() {
-	s.setHistory(nil, 0)
+	mlx.Unpin(s.history)
+	*s = Sampler{
+		byID:        make(map[int]*slotState),
+		allSameOpts: true,
+		numCtx:      s.numCtx,
+	}
 }
 
-// Sample runs the configured transform chain on the raw per-token logits
-// and returns the sampled token id plus, when configured, the reported
-// log-probability tensors for the selected token and the top-K tokens.
-func (s *Sampler) Sample(logits *mlx.Array) Result {
-	scores := logits
-	for _, transform := range s.transforms {
-		scores = transform(s, scores)
+// Sample draws one token per row of logits ([B,V]); seqIDs[i] names the
+// slot whose logits live at row i. Each sampled token is appended to its
+// slot's ring. Slots not named in seqIDs are untouched.
+func (s *Sampler) Sample(seqIDs []int, logits *mlx.Array) Result {
+	if len(seqIDs) == 0 {
+		return Result{}
 	}
-	res := Result{Token: scores}
 
-	if s.Logprobs {
-		// Compute log_softmax in fp32 and subtract the max before
-		// logsumexp so the final subtraction stays on small values.
-		// Otherwise it cancels two large numbers and loses precision.
+	slots := make([]*slotState, len(seqIDs))
+	for i, id := range seqIDs {
+		slot, ok := s.byID[id]
+		if !ok {
+			panic(fmt.Sprintf("sample.Sampler.Sample: seqID %d not registered", id))
+		}
+		slots[i] = slot
+	}
+
+	var token *mlx.Array
+	if opts0, ok := s.canBatch(slots); ok {
+		token = s.sampleTokensUniform(slots, opts0, logits)
+	} else {
+		token = s.sampleTokensSerial(slots, logits)
+	}
+
+	res := Result{Token: token}
+	if s.anyLogprobs {
+		// Log-softmax over original logits so every row holds a truthful
+		// value (compute-for-all; consumers filter per-slot). Subtract
+		// max first for numerical stability in the logsumexp.
 		lp := logits.AsType(mlx.DTypeFloat32)
 		lp = lp.Subtract(lp.MaxAxis(-1, true))
-		lp = lp.Subtract(lp.Logsumexp(true))
-		res.Logprob = lp.TakeAlongAxis(res.Token.ExpandDims(-1), -1)
-		if k := s.TopLogprobs; k > 0 {
+		lp = lp.Subtract(lp.LogsumexpAxis(-1, true))
+		res.Logprob = lp.TakeAlongAxis(token.ExpandDims(-1), -1)
+		if s.maxTopLogprobs > 0 {
+			k := s.maxTopLogprobs
 			if vocab := lp.Dim(lp.NumDims() - 1); k > vocab {
 				k = vocab
 			}
@@ -176,55 +349,180 @@ func (s *Sampler) Sample(logits *mlx.Array) Result {
 	return res
 }
 
-func greedy(_ *Sampler, scores *mlx.Array) *mlx.Array {
-	return scores.Argmax(-1, false)
+// canBatch reports whether the call can take the uniform batched path.
+// All slots must share Options; when penalties are active the call must
+// additionally cover every registered slot in registration order with a
+// full ring, because the uniform path indexes the pool positionally.
+func (s *Sampler) canBatch(slots []*slotState) (Options, bool) {
+	if !s.allSameOpts {
+		return Options{}, false
+	}
+	// slots is non-empty (Sample guards) and every slot is registered,
+	// so s.slots[0].opts is the canonical shared value.
+	shared := s.slots[0].opts
+	if !shared.usesHistory() {
+		return shared, true
+	}
+	if len(slots) != len(s.slots) {
+		return Options{}, false
+	}
+	for i, slot := range slots {
+		if s.slots[i] != slot || slot.historyLen < shared.RepeatLastN {
+			return Options{}, false
+		}
+	}
+	return shared, true
 }
 
-func temperature(s *Sampler, scores *mlx.Array) *mlx.Array {
-	return mlx.DivScalar(scores, s.Temperature).Categorical(-1)
+// sampleTokensUniform runs one fused transform pass over the whole batch.
+// Reached only when canBatch is true, which lets the pool be used in place
+// with a single PutAlongAxis write-back and no gather.
+func (s *Sampler) sampleTokensUniform(slots []*slotState, opts Options, logits *mlx.Array) *mlx.Array {
+	B := len(slots)
+
+	var hist *mlx.Array
+	if opts.usesHistory() {
+		hist = s.history
+		if s.historyWidth() > opts.RepeatLastN {
+			hist = hist.Slice(mlx.Slice(), mlx.Slice(0, opts.RepeatLastN))
+		}
+	}
+
+	ctx := &slotCtx{opts: opts, history: hist}
+	scores := logits
+	for _, t := range slots[0].transforms {
+		scores = t(ctx, scores)
+	}
+	token := scores
+
+	if !opts.usesHistory() {
+		return token
+	}
+
+	writeIdxData := make([]int32, B)
+	for i, slot := range slots {
+		writeIdxData[i] = int32(slot.historyLen % opts.RepeatLastN)
+		slot.historyLen++
+	}
+	writeIdx := mlx.NewArrayInt32(writeIdxData, []int32{int32(B), 1})
+
+	s.history.Set(s.history.PutAlongAxis(writeIdx, token.ExpandDims(-1), 1))
+	return token
+}
+
+// sampleTokensSerial runs each slot's transforms against its own row of
+// logits.
+func (s *Sampler) sampleTokensSerial(slots []*slotState, logits *mlx.Array) *mlx.Array {
+	perSlotTokens := make([]*mlx.Array, len(slots))
+
+	rowOf := make(map[*slotState]int, len(s.slots))
+	for i, slot := range s.slots {
+		rowOf[slot] = i
+	}
+
+	for i, slot := range slots {
+		row := logits.Slice(mlx.Slice(i, i+1), mlx.Slice())
+
+		var hist *mlx.Array
+		if slot.opts.usesHistory() && slot.historyLen > 0 && s.history != nil {
+			poolRow := rowOf[slot]
+			fill := min(slot.historyLen, slot.opts.RepeatLastN)
+			hist = s.history.Slice(
+				mlx.Slice(poolRow, poolRow+1),
+				mlx.Slice(0, fill),
+			)
+		}
+
+		ctx := &slotCtx{opts: slot.opts, history: hist}
+		scores := row
+		for _, t := range slot.transforms {
+			scores = t(ctx, scores)
+		}
+		perSlotTokens[i] = scores
+	}
+
+	token := mlx.Concatenate(perSlotTokens, 0)
+
+	if s.history != nil {
+		// For each writing slot collect its flat (row-major) pool offset
+		// and the call-order position of its token. One PutAlongAxis on a
+		// flat view of the pool scatters all writes in a single op.
+		flatOffsets := make([]int32, 0, len(slots))
+		tokenPos := make([]int32, 0, len(slots))
+		for i, slot := range slots {
+			if !slot.opts.usesHistory() {
+				continue
+			}
+			ringPos := slot.historyLen % slot.opts.RepeatLastN
+			flatOffsets = append(flatOffsets, int32(rowOf[slot]*s.historyWidth()+ringPos))
+			tokenPos = append(tokenPos, int32(i))
+			slot.historyLen++
+		}
+
+		if len(flatOffsets) > 0 {
+			m := len(flatOffsets)
+			flatIdx := mlx.NewArrayInt32(flatOffsets, []int32{int32(m), 1})
+			writingTokens := token
+			if m != len(slots) {
+				tokenPosIdx := mlx.NewArrayInt32(tokenPos, []int32{int32(m)})
+				writingTokens = token.TakeAxis(tokenPosIdx, 0)
+			}
+			flatHist := s.history.Reshape(s.history.Dim(0)*s.historyWidth(), 1)
+			s.history.Set(flatHist.PutAlongAxis(flatIdx, writingTokens.ExpandDims(-1), 0).Reshape(s.history.Dim(0), s.historyWidth()))
+		}
+	}
+	return token
+}
+
+func greedy(_ *slotCtx, scores *mlx.Array) *mlx.Array {
+	return scores.Argmax(-1, false).AsType(mlx.DTypeInt32)
+}
+
+func temperature(ctx *slotCtx, scores *mlx.Array) *mlx.Array {
+	return mlx.DivScalar(scores, ctx.opts.Temperature).Categorical(-1).AsType(mlx.DTypeInt32)
 }
 
 // topKTopP applies top-P in a descending sort pass and, when top-K is also
 // configured, masks any surviving value below the K-th largest in the same
-// pass. Callers dispatch here whenever top-P is enabled — the top-K-only
-// case uses a cheaper partial sort via the topK transform.
-func topKTopP(s *Sampler, scores *mlx.Array) *mlx.Array {
+// pass. Callers dispatch here whenever top-P is enabled — the top-K-only case
+// uses a cheaper partial sort via the topK transform.
+func topKTopP(ctx *slotCtx, scores *mlx.Array) *mlx.Array {
 	vocab := scores.Dim(scores.NumDims() - 1)
-	applyTopK := s.TopK > 0 && s.TopK < vocab
+	applyTopK := ctx.opts.TopK > 0 && ctx.opts.TopK < vocab
 
 	order := scores.Negative().ArgsortAxis(-1)
 	sorted := scores.TakeAlongAxis(order, -1)
 	negInf := mlx.FromValue(float32(math.Inf(-1)))
 
 	// Top-P: in descending order, keep tokens whose exclusive cumulative
-	// probability is still below s.TopP.
+	// probability is still below TopP.
 	probs := mlx.SoftmaxAxis(sorted, -1, true)
 	prevCumProbs := probs.Cumsum(-1, false, true).Subtract(probs)
-	keep := prevCumProbs.Less(mlx.FromValue(s.TopP))
+	keep := prevCumProbs.Less(mlx.FromValue(ctx.opts.TopP))
 	sorted = mlx.Where(keep, sorted, negInf)
 
 	out := scores.PutAlongAxis(order, sorted, -1)
 
-	// Top-K: sorted is already in descending order, so positions [K, V)
-	// are the ones to drop. Scatter -inf through their original-layout
-	// indices (order[K:]). Positional (not value-based) so exactly K
-	// tokens survive — ties at the K-th logit get broken by the sort
-	// order rather than promoted through the filter.
+	// Top-K: sorted is already in descending order, so positions [K, V) are
+	// the ones to drop. Scatter -inf through their original-layout indices
+	// (order[K:]). Positional (not value-based) so exactly K tokens survive —
+	// ties at the K-th logit get broken by the sort order rather than
+	// promoted through the filter.
 	if applyTopK {
-		dropOrder := order.Slice(mlx.Slice(), mlx.Slice(s.TopK, mlx.End))
+		dropOrder := order.Slice(mlx.Slice(), mlx.Slice(ctx.opts.TopK, mlx.End))
 		out = out.PutAlongAxis(dropOrder, negInf, -1)
 	}
 
 	return out
 }
 
-func minP(s *Sampler, scores *mlx.Array) *mlx.Array {
-	if s.MinP <= 0 || s.MinP > 1 {
+func minP(ctx *slotCtx, scores *mlx.Array) *mlx.Array {
+	if ctx.opts.MinP <= 0 || ctx.opts.MinP > 1 {
 		return scores
 	}
 
 	maxScore := scores.MaxAxis(-1, true)
-	threshold := mlx.AddScalar(maxScore, float32(math.Log(float64(s.MinP))))
+	threshold := mlx.AddScalar(maxScore, float32(math.Log(float64(ctx.opts.MinP))))
 
 	return mlx.Where(
 		scores.Less(threshold),
@@ -233,48 +531,43 @@ func minP(s *Sampler, scores *mlx.Array) *mlx.Array {
 	)
 }
 
-func topK(s *Sampler, scores *mlx.Array) *mlx.Array {
-	if s.TopK <= 0 {
+func topK(ctx *slotCtx, scores *mlx.Array) *mlx.Array {
+	if ctx.opts.TopK <= 0 {
 		return scores
 	}
-
 	vocab := scores.Dim(scores.NumDims() - 1)
-	if s.TopK >= vocab {
+	if ctx.opts.TopK >= vocab {
 		return scores
 	}
 
-	mask := scores.Negative().ArgpartitionAxis(s.TopK-1, -1).Slice(mlx.Slice(), mlx.Slice(s.TopK, mlx.End))
+	mask := scores.Negative().ArgpartitionAxis(ctx.opts.TopK-1, -1).Slice(mlx.Slice(), mlx.Slice(ctx.opts.TopK, mlx.End))
 	return scores.PutAlongAxis(mask, mlx.FromValue(float32(math.Inf(-1))), -1)
 }
 
-func penalty(s *Sampler, scores *mlx.Array) *mlx.Array {
-	if s.historyLen == 0 {
+func penalty(ctx *slotCtx, scores *mlx.Array) *mlx.Array {
+	tokenIndices := ctx.history
+	if tokenIndices == nil {
 		return scores
 	}
 
-	tokenIndices := s.history
-	if scores.NumDims() > 1 {
-		tokenIndices = tokenIndices.ExpandDims(0)
-	}
-
-	if s.RepeatPenalty != 1 || s.PresencePenalty != 0 {
+	if ctx.opts.RepeatPenalty != 1 || ctx.opts.PresencePenalty != 0 {
 		adjusted := scores.TakeAlongAxis(tokenIndices, -1)
-		if s.RepeatPenalty != 1 {
+		if ctx.opts.RepeatPenalty != 1 {
 			factor := mlx.Where(
 				adjusted.Less(mlx.FromValue(float32(0))),
-				mlx.FromValue(s.RepeatPenalty),
-				mlx.FromValue(1/s.RepeatPenalty),
+				mlx.FromValue(ctx.opts.RepeatPenalty),
+				mlx.FromValue(1/ctx.opts.RepeatPenalty),
 			)
 			adjusted = adjusted.Multiply(factor)
 		}
-		if s.PresencePenalty != 0 {
-			adjusted = mlx.AddScalar(adjusted, -s.PresencePenalty)
+		if ctx.opts.PresencePenalty != 0 {
+			adjusted = mlx.AddScalar(adjusted, -ctx.opts.PresencePenalty)
 		}
 		scores = scores.PutAlongAxis(tokenIndices, adjusted, -1)
 	}
 
-	if s.FrequencyPenalty != 0 {
-		scores = scores.ScatterAddAxis(tokenIndices, mlx.FromValue(-s.FrequencyPenalty), -1)
+	if ctx.opts.FrequencyPenalty != 0 {
+		scores = scores.ScatterAddAxis(tokenIndices, mlx.FromValue(-ctx.opts.FrequencyPenalty), -1)
 	}
 
 	return scores

x/mlxrunner/sample/sample_test.go

@@ -9,93 +9,283 @@ import (
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
 )
 
-func TestPresencePenaltyUsesAppendedTokenImmediately(t *testing.T) {
-	s := New(Options{RepeatLastN: 1, PresencePenalty: 6})
-	defer func() {
+// slotLogits builds a [1, V] logits tensor for a single-slot Sample call.
+func slotLogits(values []float32) *mlx.Array {
+	return mlx.FromValues(values, 1, len(values))
+}
+
+// batchLogits stacks per-row float32 slices of equal length into a [B, V]
+// logits tensor.
+func batchLogits(rows ...[]float32) *mlx.Array {
+	v := len(rows[0])
+	flat := make([]float32, 0, len(rows)*v)
+	for _, r := range rows {
+		if len(r) != v {
+			panic("batchLogits: rows must share vocab size")
+		}
+		flat = append(flat, r...)
+	}
+	return mlx.FromValues(flat, len(rows), v)
+}
+
+// sampleOne runs Sample on a freshly-added single slot and returns the
+// sampled token id. Used both for the single-slot options table and as the
+// reference oracle for the batched-equivalence test.
+func sampleOne(t *testing.T, opts Options, priorTokens []int32, values []float32) int {
+	t.Helper()
+	s := New(128)
+	t.Cleanup(func() {
 		s.Free()
 		mlx.Sweep()
-	}()
+	})
+	s.Add(0, opts, priorTokens)
 
-	s.ResetHistory([]int32{0})
-	s.AppendToken(mlx.NewArrayInt32([]int32{1}, []int32{1}))
-
-	logits := mlx.FromValues([]float32{0, 5, 4}, 3)
-	got := s.Sample(logits).Token
+	got := s.Sample([]int{0}, slotLogits(values)).Token
 	mlx.Eval(got)
+	return got.Int()
+}
 
-	// logits will be [0, -1, 4] after the penalty
-	// and then (index) 2 after the greedy sampler
-	gotInt := got.Int()
-	if gotInt != 2 {
-		t.Fatalf("got %d, want 2", gotInt)
+// logOf returns log(p) as a float32 so tests can build logits that softmax to
+// a chosen probability distribution.
+func logOf(p float64) float32 { return float32(math.Log(p)) }
+
+// TestSampleSingleSlotOptions pins the per-slot behavior of each Options
+// knob against a concrete expected token. Expected values are worked out by
+// hand from the math of each transform, not from a second call into the
+// sampler — so a regression in any single transform shows up here.
+func TestSampleSingleSlotOptions(t *testing.T) {
+	cases := []struct {
+		name   string
+		opts   Options
+		priors []int32
+		logits []float32
+		want   int
+	}{
+		{
+			name:   "presence penalty",
+			opts:   Options{RepeatLastN: 1, PresencePenalty: 6},
+			priors: []int32{1},
+			logits: []float32{0, 5, 4},
+			want:   2, // token 1: 5 - 6 = -1, argmax shifts to 2
+		},
+		{
+			name:   "repeat penalty on positive logits",
+			opts:   Options{RepeatLastN: 1, RepeatPenalty: 2},
+			priors: []int32{1},
+			logits: []float32{0, 5, 4},
+			want:   2, // token 1 positive → divided: 5/2 = 2.5, argmax shifts to 2
+		},
+		{
+			name:   "repeat penalty on negative logits",
+			opts:   Options{RepeatLastN: 1, RepeatPenalty: 4},
+			priors: []int32{1},
+			logits: []float32{-5, -1, -3},
+			want:   2, // token 1 negative → multiplied: -1*4 = -4, argmax shifts to 2
+		},
+		{
+			name:   "frequency penalty",
+			opts:   Options{RepeatLastN: 4, FrequencyPenalty: 2},
+			priors: []int32{1, 1},
+			logits: []float32{0, 5, 4},
+			want:   2, // 5 - 2*count(1)=2*2=4 → 1, argmax shifts to 2
+		},
+		{
+			name:   "top-k",
+			opts:   Options{Temperature: 1, TopK: 1},
+			logits: []float32{1, 5, 4},
+			want:   1, // only argmax survives → deterministic even with temperature
+		},
+		{
+			name:   "top-p",
+			opts:   Options{Temperature: 1, TopP: 0.4},
+			logits: []float32{logOf(0.5), logOf(0.3), logOf(0.2)},
+			want:   0, // exclusive cumsum below 0.4 keeps only token 0
+		},
+		{
+			name:   "min-p",
+			opts:   Options{Temperature: 1, MinP: 0.7},
+			logits: []float32{logOf(0.5), logOf(0.3), logOf(0.2)},
+			want:   0, // threshold 0.5*0.7=0.35 drops all but the top token
+		},
+		{
+			name:   "RepeatLastN=0 disables penalties",
+			opts:   Options{RepeatLastN: 0, RepeatPenalty: 2, PresencePenalty: 10},
+			priors: []int32{1},
+			logits: []float32{0, 5, 4},
+			want:   1, // 0 = disabled per API contract, argmax unchanged
+		},
+		{
+			name:   "RepeatLastN=-1 resolves to num_ctx",
+			opts:   Options{RepeatLastN: -1, PresencePenalty: 6},
+			priors: []int32{1},
+			logits: []float32{0, 5, 4},
+			want:   2, // -1 → num_ctx (128); penalty applies, argmax shifts
+		},
+	}
+
+	for _, tc := range cases {
+		t.Run(tc.name, func(t *testing.T) {
+			if got := sampleOne(t, tc.opts, tc.priors, tc.logits); got != tc.want {
+				t.Errorf("got %d, want %d", got, tc.want)
+			}
+		})
 	}
 }
 
-func TestRepeatPenaltyUsesHistoryWithoutPresencePenalty(t *testing.T) {
-	s := New(Options{RepeatLastN: 1, RepeatPenalty: 2})
-	defer func() {
+// TestSampleHistoryWindow verifies that penalty history respects the
+// RepeatLastN window: priors longer than RepeatLastN are trimmed on Add,
+// and once the ring wraps, tokens that rotate out no longer contribute
+// to penalties.
+func TestSampleHistoryWindow(t *testing.T) {
+	s := New(128)
+	t.Cleanup(func() {
 		s.Free()
 		mlx.Sweep()
-	}()
+	})
 
-	s.ResetHistory([]int32{1})
+	// RepeatLastN=2 with priors {1, 2, 3}: makeHistoryRow keeps only
+	// {2, 3}. Token 1 was trimmed — its penalty is NOT active.
+	s.Add(0, Options{RepeatLastN: 2, PresencePenalty: 10}, []int32{1, 2, 3})
 
-	logits := mlx.FromValues([]float32{0, 5, 4}, 3)
-	got := s.Sample(logits).Token
-	mlx.Eval(got)
+	// Step 1: logits favor token 1 (trimmed). If the trim were broken it
+	// would be penalized and the argmax would move.
+	step1 := s.Sample([]int{0}, slotLogits([]float32{0, 5, 0, 0, 0})).Token
+	mlx.Eval(step1)
+	if got := step1.Int(); got != 1 {
+		t.Fatalf("step 1 = %d, want 1 (token 1 trimmed from priors)", got)
+	}
+	// After step 1 the ring holds {1, 3}; token 2 has rotated out.
 
-	// token 1 is repeated and positive, so 5 / 2 falls below token 2.
-	gotInt := got.Int()
-	if gotInt != 2 {
-		t.Fatalf("got %d, want 2", gotInt)
+	// Step 2: logits favor token 2 (rotated out). If the ring wrap were
+	// wrong, token 2 would still be penalized.
+	step2 := s.Sample([]int{0}, slotLogits([]float32{0, 0, 5, 0, 0})).Token
+	mlx.Eval(step2)
+	if got := step2.Int(); got != 2 {
+		t.Fatalf("step 2 = %d, want 2 (token 2 rotated out of ring)", got)
 	}
 }
 
-func TestFrequencyPenaltyUsesTokenCounts(t *testing.T) {
-	s := New(Options{RepeatLastN: 4, FrequencyPenalty: 2})
-	defer func() {
-		s.Free()
-		mlx.Sweep()
-	}()
+// TestBatchSamplingPreservesPerSlotBehavior is the core equivalence test:
+// for every representative dispatch branch (uniform, serial on mixed opts,
+// serial on partial ring, subset/out-of-order), a batched Sample call must
+// produce the same token per row as running the same slot alone.
+func TestBatchSamplingPreservesPerSlotBehavior(t *testing.T) {
+	type slot struct {
+		id     int
+		opts   Options
+		priors []int32
+	}
 
-	s.ResetHistory([]int32{1, 1})
+	cases := []struct {
+		name   string
+		slots  []slot
+		sample []int
+		rows   [][]float32
+	}{
+		{
+			name: "uniform",
+			slots: []slot{
+				{10, Options{RepeatLastN: 2, PresencePenalty: 5}, []int32{1, 2}},
+				{20, Options{RepeatLastN: 2, PresencePenalty: 5}, []int32{0, 2}},
+			},
+			sample: []int{10, 20},
+			rows:   [][]float32{{0, 5, 4}, {3, 0, 0}},
+		},
+		{
+			name: "serial — mixed opts",
+			slots: []slot{
+				{1, Options{RepeatLastN: 1, RepeatPenalty: 2}, []int32{1}},
+				{2, Options{Temperature: 1, TopK: 1}, nil},
+			},
+			sample: []int{1, 2},
+			rows:   [][]float32{{0, 5, 4, 1}, {2, 1, 5, 3}},
+		},
+		{
+			name: "serial — partial ring",
+			slots: []slot{
+				{1, Options{RepeatLastN: 4, PresencePenalty: 5}, []int32{1, 1, 1, 1}},
+				{2, Options{RepeatLastN: 4, PresencePenalty: 5}, []int32{2}},
+			},
+			sample: []int{1, 2},
+			rows:   [][]float32{{0, 5, 4}, {0, 4, 5}},
+		},
+		{
+			name: "subset out-of-order",
+			slots: []slot{
+				{10, Options{RepeatLastN: 2, PresencePenalty: 10}, []int32{1, 1}},
+				{20, Options{RepeatLastN: 2, PresencePenalty: 10}, []int32{2, 2}},
+				{30, Options{RepeatLastN: 2, PresencePenalty: 10}, []int32{3, 3}},
+			},
+			sample: []int{30, 10},
+			rows:   [][]float32{{5, 5, 5, 0, 5, 5}, {5, 0, 5, 5, 0, 5}},
+		},
+	}
 
-	logits := mlx.FromValues([]float32{0, 5, 4}, 3)
-	got := s.Sample(logits).Token
-	mlx.Eval(got)
+	for _, tc := range cases {
+		t.Run(tc.name, func(t *testing.T) {
+			// Per-slot reference for each sampled seq.
+			want := make([]int, len(tc.sample))
+			for i, id := range tc.sample {
+				var spec slot
+				for _, s := range tc.slots {
+					if s.id == id {
+						spec = s
+						break
+					}
+				}
+				want[i] = sampleOne(t, spec.opts, spec.priors, tc.rows[i])
+			}
 
-	// token 1 appears twice, so 5 - (2 * 2) falls below token 2.
-	gotInt := got.Int()
-	if gotInt != 2 {
-		t.Fatalf("got %d, want 2", gotInt)
+			// Batched call.
+			s := New(128)
+			t.Cleanup(func() {
+				s.Free()
+				mlx.Sweep()
+			})
+			for _, spec := range tc.slots {
+				s.Add(spec.id, spec.opts, spec.priors)
+			}
+			res := s.Sample(tc.sample, batchLogits(tc.rows...))
+			mlx.Eval(res.Token)
+			got := res.Token.Ints()
+
+			for i, id := range tc.sample {
+				if got[i] != want[i] {
+					t.Errorf("seq %d: batched = %d, per-slot = %d", id, got[i], want[i])
+				}
+			}
+		})
 	}
 }
 
-func TestMinPMasksTokensBelowThreshold(t *testing.T) {
-	s := New(Options{MinP: 0.5})
-	defer func() {
+// TestRemoveDoesNotLeakHistory: after Remove, a newly-added slot at the
+// recycled row must start from its own priors only — no carryover from
+// the removed slot's history.
+func TestRemoveDoesNotLeakHistory(t *testing.T) {
+	opts := Options{RepeatLastN: 1, PresencePenalty: 10}
+	s := New(128)
+	t.Cleanup(func() {
 		s.Free()
 		mlx.Sweep()
-	}()
+	})
+	s.Add(1, opts, []int32{1})
+	s.Add(2, opts, []int32{2})
+	s.Remove(1)
+	s.Add(3, opts, []int32{0})
 
-	logits := mlx.FromValues([]float32{
-		float32(math.Log(0.5)),
-		float32(math.Log(0.3)),
-		float32(math.Log(0.2)),
-	}, 3)
-	got := minP(s, logits)
-	mlx.Eval(got)
-
-	gotFloats := got.Floats()
-	if len(gotFloats) != 3 {
-		t.Fatalf("got %d scores, want 3", len(gotFloats))
+	// Slot 2 retains history {2}; slot 3 retains history {0}. With
+	// equal logits and PresencePenalty=10 the argmax drops to the first
+	// unpenalized token.
+	res := s.Sample([]int{2, 3}, batchLogits(
+		[]float32{3, 3, 0},
+		[]float32{3, 3, 0},
+	))
+	mlx.Eval(res.Token)
+	tokens := res.Token.Ints()
+	if tokens[0] != 0 {
+		t.Errorf("slot 2 = %d, want 0 (token 2 penalized)", tokens[0])
 	}
-
-	if math.IsInf(float64(gotFloats[0]), -1) || math.IsInf(float64(gotFloats[1]), -1) {
-		t.Fatalf("kept tokens were masked: %v", gotFloats)
-	}
-
-	if !math.IsInf(float64(gotFloats[2]), -1) {
-		t.Fatalf("lowest-probability token should be masked, got %v", gotFloats)
+	if tokens[1] != 1 {
+		t.Errorf("slot 3 = %d, want 1 (token 0 penalized, no slot-1 carryover)", tokens[1])
 	}
 }

x/mlxrunner/server.go

@@ -93,7 +93,7 @@ func Execute(args []string) error {
 		}
 
 		request.Pipeline = runner.TextGenerationPipeline
-		request.Sampler = sample.New(sample.Options{
+		request.SamplerOpts = sample.Options{
 			Temperature:      request.Options.Temperature,
 			TopP:             request.Options.TopP,
 			MinP:             request.Options.MinP,
@@ -104,7 +104,12 @@ func Execute(args []string) error {
 			FrequencyPenalty: request.Options.FrequencyPenalty,
 			Logprobs:         request.Logprobs,
 			TopLogprobs:      request.TopLogprobs,
-		})
+		}
+
+		if err := runner.Prepare(&request); err != nil {
+			http.Error(w, err.Error(), http.StatusBadRequest)
+			return
+		}
 
 		var cancel context.CancelFunc
 		request.Ctx, cancel = context.WithCancel(r.Context())