diff --git a/x/mlxrunner/mlx/array.go b/x/mlxrunner/mlx/array.go
index 198162efd..00bf92153 100644
--- a/x/mlxrunner/mlx/array.go
+++ b/x/mlxrunner/mlx/array.go
@@ -10,6 +10,8 @@ import (
 	"reflect"
 	"sort"
 	"strings"
+	"sync"
+	"sync/atomic"
 	"unsafe"
 
 	"github.com/ollama/ollama/logutil"
@@ -18,15 +20,20 @@ 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}
+	arraysMu.Lock()
+	defer arraysMu.Unlock()
 	arrays = append(arrays, t)
 	return t
 }
@@ -127,7 +134,7 @@ func (t *Array) Clone() *Array {
 func Pin(s ...*Array) {
 	for _, t := range s {
 		if t != nil {
-			t.pinned++
+			t.pinned.Add(1)
 		}
 	}
 }
@@ -136,8 +143,7 @@ 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))
 			}
 		}
@@ -147,9 +153,11 @@ 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() {
@@ -176,7 +184,7 @@ 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,
@@ -190,19 +198,19 @@ 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)
@@ -211,29 +219,29 @@ 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 {
 	ints := make([]int, t.Size())
 	for i, f := range unsafe.Slice(C.mlx_array_data_int32(t.ctx), len(ints)) {
 		ints[i] = int(f)
@@ -241,7 +249,7 @@ func (t Array) Ints() []int {
 	return ints
 }
 
-func (t Array) Floats() []float32 {
+func (t *Array) Floats() []float32 {
 	floats := make([]float32, t.Size())
 	for i, f := range unsafe.Slice(C.mlx_array_data_float32(t.ctx), len(floats)) {
 		floats[i] = float32(f)
@@ -249,7 +257,7 @@ 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)
@@ -258,6 +266,8 @@ 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()
 	})
@@ -266,7 +276,7 @@ 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())))
 }
diff --git a/x/mlxrunner/mlx/fast.go b/x/mlxrunner/mlx/fast.go
index 7feca3b1e..d5b218d1c 100644
--- a/x/mlxrunner/mlx/fast.go
+++ b/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
diff --git a/x/mlxrunner/mlx/nn.go b/x/mlxrunner/mlx/nn.go
index d3a99a6cd..d2e7fb4f1 100644
--- a/x/mlxrunner/mlx/nn.go
+++ b/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 {