diff --git a/lib/mpsgraphs/matmul.jl b/lib/mpsgraphs/matmul.jl
index 2bbc1d2e1..55720066e 100644
--- a/lib/mpsgraphs/matmul.jl
+++ b/lib/mpsgraphs/matmul.jl
@@ -30,20 +30,71 @@ else
 end
 
 
-@autoreleasepool function _matmul!(c::MtlArray{Tc}, a::MtlArray{Tab, Na}, b::MtlArray{Tab, Nb},
-                                   alpha::Number, beta::Number,
-                                   transpose_a, transpose_b) where {Tc, Tab, Na, Nb}
-    graph = MPSGraph()
+#=
+MPSGraph caching infrastructure.
 
-    placeA = placeholderTensor(graph, size(a), Tab)
-    placeB = placeholderTensor(graph, size(b), Tab)
-    placeC = placeholderTensor(graph, size(c), Tc)
+Creating an MPSGraph takes ~2ms per call, which dominates matmul time for small-medium
+matrices. By caching graphs keyed by their structural parameters (shapes, types, flags),
+we achieve significant speedup for repeated operations with the same configuration.
 
-    feeds = Dict{MPSGraphTensor, MPSGraphTensorData}(
-        placeA => MPSGraphTensorData(a),
-        placeB => MPSGraphTensorData(b),
-        placeC => MPSGraphTensorData(c)
+The cache key includes all parameters that affect graph structure:
+- Input/output shapes and element types
+- Transpose flags
+- Alpha/beta values (baked into graph as constants)
+=#
+
+# Cache key for matmul graphs - includes all structural parameters
+struct MatmulGraphKey
+    size_a::Tuple{Vararg{Int}}
+    size_b::Tuple{Vararg{Int}}
+    size_c::Tuple{Vararg{Int}}
+    eltype_ab::DataType
+    eltype_c::DataType
+    ndims_a::Int
+    ndims_b::Int
+    alpha::Float64  # Normalized to Float64 for hashing
+    beta::Float64
+    transpose_a::Bool
+    transpose_b::Bool
+end
+
+# Cached graph with all tensors needed for execution
+struct CachedMatmulGraph
+    graph::MPSGraph
+    place_c::MPSGraphTensor
+    place_a::MPSGraphTensor
+    place_b::MPSGraphTensor
+    result::MPSGraphTensor
+end
+
+# Thread-safe graph cache with lock
+const _matmul_graph_cache = Dict{MatmulGraphKey, CachedMatmulGraph}()
+const _matmul_graph_cache_lock = ReentrantLock()
+
+# Build graph key from matmul parameters
+function _make_matmul_key(a::MtlArray{Tab, Na}, b::MtlArray{Tab, Nb}, c::MtlArray{Tc},
+                          alpha::Number, beta::Number,
+                          transpose_a::Bool, transpose_b::Bool) where {Tc, Tab, Na, Nb}
+    MatmulGraphKey(
+        size(a), size(b), size(c),
+        Tab, Tc,
+        Na, Nb,
+        Float64(alpha), Float64(beta),
+        transpose_a, transpose_b
     )
+end
+
+# Build a new matmul graph (called only on cache miss)
+function _build_matmul_graph(size_a::Tuple, size_b::Tuple, size_c::Tuple,
+                             Tab::DataType, Tc::DataType,
+                             Na::Int, Nb::Int,
+                             transpose_a::Bool, transpose_b::Bool,
+                             alpha::Number, beta::Number)
+    graph = MPSGraph()
+
+    placeA = placeholderTensor(graph, size_a, Tab)
+    placeB = placeholderTensor(graph, size_b, Tab)
+    placeC = placeholderTensor(graph, size_c, Tc)
 
     # cast to output eltype if input type is an integer type
     castT = Tab <: Integer ? Tc : Tab
@@ -53,8 +104,8 @@ end
     transA = transpose_a ? transposeTensor(graph, castA, Na-2, Na-1, "transpose_a") : castA
     transB = transpose_b ? transposeTensor(graph, castB, Nb-2, Nb-1, "transpose_b") : castB
 
-    nBatchA = Na == 2 ? 1 : size(transA)[1]
-    nBatchB = Nb == 2 ? 1 : size(transB)[1]
+    nBatchA = Na == 2 ? 1 : size_a[1]
+    nBatchB = Nb == 2 ? 1 : size_b[1]
 
     # for batched matmul between different sized tensors
     broadcastA, broadcastB = if nBatchA == nBatchB
@@ -81,12 +132,58 @@ end
 
     castC = castTensor(graph, afterbeta, Tc, "castC")
 
+    CachedMatmulGraph(graph, placeC, placeA, placeB, castC)
+end
+
+# Get or create cached graph
+function _get_cached_graph(key::MatmulGraphKey)
+    # Fast path: check cache without lock (safe for reads)
+    cached = get(_matmul_graph_cache, key, nothing)
+    if cached !== nothing
+        return cached
+    end
+
+    # Slow path: acquire lock and build graph
+    lock(_matmul_graph_cache_lock) do
+        # Double-check after acquiring lock
+        cached = get(_matmul_graph_cache, key, nothing)
+        if cached !== nothing
+            return cached
+        end
+
+        # Build new graph
+        cached = _build_matmul_graph(
+            key.size_a, key.size_b, key.size_c,
+            key.eltype_ab, key.eltype_c,
+            key.ndims_a, key.ndims_b,
+            key.transpose_a, key.transpose_b,
+            key.alpha, key.beta
+        )
+        _matmul_graph_cache[key] = cached
+        return cached
+    end
+end
+
+@autoreleasepool function _matmul!(c::MtlArray{Tc}, a::MtlArray{Tab, Na}, b::MtlArray{Tab, Nb},
+                                   alpha::Number, beta::Number,
+                                   transpose_a, transpose_b) where {Tc, Tab, Na, Nb}
+    # Get or create cached graph
+    key = _make_matmul_key(a, b, c, alpha, beta, transpose_a, transpose_b)
+    cached = _get_cached_graph(key)
+
+    # Build feed and result dictionaries with current data
+    feeds = Dict{MPSGraphTensor, MPSGraphTensorData}(
+        cached.place_a => MPSGraphTensorData(a),
+        cached.place_b => MPSGraphTensorData(b),
+        cached.place_c => MPSGraphTensorData(c)
+    )
+
     resultdict = Dict{MPSGraphTensor, MPSGraphTensorData}(
-        castC => feeds[placeC]
+        cached.result => MPSGraphTensorData(c)
     )
 
     cmdbuf = MPSCommandBuffer(Metal.global_queue(device()))
-    encode!(cmdbuf, graph, NSDictionary(feeds), NSDictionary(resultdict), nil, default_exec_desc())
+    encode!(cmdbuf, cached.graph, NSDictionary(feeds), NSDictionary(resultdict), nil, default_exec_desc())
     commit!(cmdbuf)
     wait_completed(cmdbuf)