diff --git a/mlx/backend/metal/conv.cpp b/mlx/backend/metal/conv.cpp
index 720e2c546..35ed3d44e 100644
--- a/mlx/backend/metal/conv.cpp
+++ b/mlx/backend/metal/conv.cpp
@@ -944,8 +944,6 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
     wt = arr_copy;
   }
 
-  auto padding_ = padding_lo_;
-
   // 3D conv
   if (out.ndim() == 5) {
     conv_3D_gpu(
@@ -954,7 +952,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -969,7 +967,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -985,7 +983,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
diff --git a/python/tests/test_conv.py b/python/tests/test_conv.py
index 671c86a32..35dcf42ac 100644
--- a/python/tests/test_conv.py
+++ b/python/tests/test_conv.py
@@ -1088,6 +1088,48 @@ class TestConv(mlx_tests.MLXTestCase):
                         atol=2e-5 if dtype == np.float32 else 5e-4,
                     )
 
+    @unittest.skipIf(not has_torch, "requires Torch")
+    def test_asymmetric_padding(self):
+        inputs = np.random.normal(size=(2, 8, 8, 8, 3)).astype(np.float32)
+        kernel = np.random.normal(size=(2, 3, 3, 3, 3)).astype(np.float32)
+        strides = (2, 2, 2)
+
+        pt_out = torch.conv3d(
+            torch.permute(torch.tensor(inputs), (0, 4, 1, 2, 3)),
+            torch.permute(torch.tensor(kernel), (0, 4, 1, 2, 3)),
+            stride=strides,
+            padding=2,
+        )
+        pt_out = torch.permute(pt_out, (0, 2, 3, 4, 1))[:, 1:, 1:, 1:, :].numpy()
+
+        mx_out = mx.conv_general(
+            mx.array(inputs),
+            mx.array(kernel),
+            stride=strides,
+            padding=([0, 0, 0], [1, 1, 1]),
+        )
+
+        self.assertTrue(mx.allclose(mx_out, mx.array(pt_out), atol=1e-3, rtol=1e-3))
+
+        inputs = np.random.normal(size=(2, 10, 10, 3)).astype(np.float32)
+        kernel = np.random.normal(size=(2, 2, 2, 3)).astype(np.float32)
+
+        pt_out = torch.conv2d(
+            torch.permute(torch.tensor(inputs), (0, 3, 1, 2)),
+            torch.permute(torch.tensor(kernel), (0, 3, 1, 2)),
+            stride=1,
+            padding=(1, 0),
+        )
+        pt_out = torch.permute(pt_out, (0, 2, 3, 1))[:, 1:].numpy()
+
+        mx_out = mx.conv_general(
+            mx.array(inputs),
+            mx.array(kernel),
+            stride=1,
+            padding=([0, 0], [1, 0]),
+        )
+        self.assertTrue(mx.allclose(mx_out, mx.array(pt_out), atol=1e-3, rtol=1e-3))
+
 
 if __name__ == "__main__":
     unittest.main()