diff --git a/tests/unit/components/test_attention.py b/tests/unit/components/test_attention.py
index 572529cec..38cb3e0d3 100644
--- a/tests/unit/components/test_attention.py
+++ b/tests/unit/components/test_attention.py
@@ -128,3 +128,45 @@ def test_remove_einsum_from_complex_attn_linear():
 
     # Check if the results are the same
     assert torch.allclose(result_new, result_old, atol=1e-4)
+
+
+@pytest.mark.skipif(
+    not torch.backends.mps.is_available() and torch.__version__ != "2.8.0",
+    reason="Issue with F.linear issue exclusive to mps and PyTorch 2.8"
+    "https://github.com/pytorch/pytorch/issues/161640",
+)
+def test_cpu_mps_outputs_match():
+    torch.manual_seed(0)
+
+    cfg = {
+        "n_layers": 1,
+        "d_model": 48,
+        "n_ctx": 256,
+        "d_head": 16,
+        "n_heads": 3,
+        "load_in_4bit": False,
+        "dtype": torch.float32,
+        "act_fn": "relu",
+    }
+
+    def init_weights(attn_layer: nn.Module):
+        nn.init.normal_(attn_layer.W_Q, mean=0.0, std=0.02)
+        nn.init.normal_(attn_layer.W_K, mean=0.0, std=0.02)
+        nn.init.normal_(attn_layer.W_V, mean=0.0, std=0.02)
+        nn.init.normal_(attn_layer.W_O, mean=0.0, std=0.02)
+        return attn_layer
+
+    attn_cpu = Attention(cfg)
+    attn_cpu = init_weights(attn_cpu)
+
+    attn_mps = Attention(cfg).to("mps")
+    attn_mps.load_state_dict(attn_cpu.state_dict(), strict=True)
+
+    batch = 1
+    input_cpu = torch.randn(batch, cfg["n_ctx"], cfg["d_model"])
+    input_mps = input_cpu.to("mps")
+
+    cpu_output = attn_cpu(input_cpu, input_cpu, input_cpu)
+    mps_output = attn_mps(input_mps, input_mps, input_mps)
+
+    assert torch.allclose(cpu_output, mps_output.cpu())
diff --git a/transformer_lens/components/abstract_attention.py b/transformer_lens/components/abstract_attention.py
index 0aee43814..1f227500b 100644
--- a/transformer_lens/components/abstract_attention.py
+++ b/transformer_lens/components/abstract_attention.py
@@ -299,10 +299,13 @@ def forward(
                 if self.b_O.device != z.device:
                     z = z.to(self.b_O.device)
 
-                out = F.linear(
-                    z.reshape(z.shape[0], z.shape[1], self.cfg.d_head * self.cfg.n_heads),
-                    w,
-                    self.b_O,
+                z = einops.rearrange(
+                    z, "batch pos head_index d_head -> batch pos (head_index d_head)"
+                )
+
+                out = (
+                    einops.einsum(z, w, "batch pos d_heads, d_model d_heads -> batch pos d_model")
+                    + self.b_O
                 )
         else:
             # Explicitly calculate the attention result so it can be accessed by a hook