diff --git a/mlx/backend/metal/scaled_dot_product_attention.cpp b/mlx/backend/metal/scaled_dot_product_attention.cpp
index 9647d0884c..cc27bff2d1 100644
--- a/mlx/backend/metal/scaled_dot_product_attention.cpp
+++ b/mlx/backend/metal/scaled_dot_product_attention.cpp
@@ -394,7 +394,7 @@ void ScaledDotProductAttention::eval_gpu(
 
   // Define some copy functions to ensure the layout of the inputs is as
   // expected.
-  copies.reserve(3);
+  copies.reserve(inputs.size());
   auto copy_unless = [&copies, &s](
                          auto predicate, const array& arr) -> const array& {
     if (!predicate(arr)) {
diff --git a/python/tests/test_fast_sdpa.py b/python/tests/test_fast_sdpa.py
index 8f9cb34cfc..abc9ada9d3 100644
--- a/python/tests/test_fast_sdpa.py
+++ b/python/tests/test_fast_sdpa.py
@@ -619,6 +619,17 @@ def test_sdpa_broadcast_mask(self):
         out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=mask)
         self.assertTrue(mx.allclose(ref, out, atol=1e-4, rtol=1e-4))
 
+    def test_sdpa_noncontiguous_inputs(self):
+        mask = mx.ones(shape=(4, 1, 7, 7), dtype=mx.bool_)
+        mx.random.seed(0)
+        q = mx.random.normal(shape=(4, 7, 32, 64)).swapaxes(1, 2)
+
+        k = mx.random.normal(shape=(4, 7, 8, 64)).swapaxes(1, 2)
+        v = mx.random.normal(shape=(4, 7, 8, 64)).swapaxes(1, 2)
+        out = mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0, mask=mask)
+        ref = mlx_ref_attn(q, k, v, scale=1.0, mask=mask)
+        self.assertTrue(mx.allclose(ref, out, atol=1e-4, rtol=1e-4))
+
     def test_sdpa_promote_mask(self):
         mask = mx.array(2.0, mx.bfloat16)
         D = 64