diff --git a/test/prototype/mx_formats/test_mx_tensor.py b/test/prototype/mx_formats/test_mx_tensor.py
index 577112b16a..1a3631cc53 100644
--- a/test/prototype/mx_formats/test_mx_tensor.py
+++ b/test/prototype/mx_formats/test_mx_tensor.py
@@ -662,3 +662,57 @@ def test_to_blocked_from_blocked_roundtrip(shape, use_triton_kernel: bool):
         rtol=0.0,
         msg=f"Roundtrip failed for shape {shape} with use_triton_kernel={use_triton_kernel}",
     )
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not torch_version_at_least("2.8.0"), reason="requires PyTorch 2.8+")
+@pytest.mark.parametrize("transpose", [False, True])
+@pytest.mark.parametrize(
+    "shape",
+    (
+        (128, 64),
+        (1, 128, 64),
+    ),
+)
+def test_scale_shape_matches_qdata(transpose, shape):
+    if len(shape) == 3 and transpose:
+        pytest.skip("transpose not yet implemented for 3D MXTensor")
+
+    block_size = 32
+
+    x_hp = torch.randn(*shape, device="cuda")
+    x = MXTensor.to_mx(
+        x_hp,
+        torch.float8_e4m3fn,
+        block_size,
+        ScaleCalculationMode.FLOOR,
+    )
+
+    if len(shape) == 2:
+        m_dim, k_dim = 0, 1
+        if transpose:
+            x_hp = x_hp.t()
+            x = x.t()
+            m_dim, k_dim = 1, 0
+    else:
+        assert len(shape) == 3, "unsupported"
+        m_dim, k_dim = 1, 2
+        if transpose:
+            x_hp = x_hp.transpose(-2, -1)
+            x = x.transpose(-2, -1)
+            m_dim, k_dim = 2, 1
+
+    orig_m = x_hp.shape[m_dim]
+    expected_padded_m = orig_m
+    actual_padded_m = x.scale.shape[m_dim]
+    assert expected_padded_m == actual_padded_m, (
+        f"incompatible padded shape for dim {m_dim}: {expected_padded_m=}, {actual_padded_m=}, {x.shape}, {x.scale.shape}"
+    )
+
+    orig_k = x_hp.shape[k_dim]
+    expected_padded_k = orig_k // block_size
+    actual_padded_k = x.scale.shape[k_dim]
+
+    assert expected_padded_k == actual_padded_k, (
+        f"incompatible padded shape for dim {k_dim}: {expected_padded_k}, {actual_padded_k=}, {x.shape}, {x.scale.shape}"
+    )
diff --git a/torchao/prototype/mx_formats/kernels.py b/torchao/prototype/mx_formats/kernels.py
index 7053225521..173d99f746 100644
--- a/torchao/prototype/mx_formats/kernels.py
+++ b/torchao/prototype/mx_formats/kernels.py
@@ -1245,7 +1245,7 @@ def triton_to_mxfp8_dim1(
 
         return (
             output_col_major.t(),
-            col_scale.view(torch.float8_e8m0fnu),
+            col_scale.view(torch.float8_e8m0fnu).squeeze(-1),
         )
 
     @register_sharding(torch.ops.torchao.triton_to_mxfp8_dim1.default)
@@ -1274,7 +1274,7 @@ def triton_to_mxfp8_dim1_reference(
         scale_e8m0_dim1 = scale_e8m0_dim1.view(torch.float8_e8m0fnu)
         return (
             x_hp_d1_normalized.t(),
-            scale_e8m0_dim1.unsqueeze(-1),
+            scale_e8m0_dim1,
         )
 
     @triton.jit
diff --git a/torchao/prototype/mx_formats/mx_tensor.py b/torchao/prototype/mx_formats/mx_tensor.py
index 05c8fdc8e4..a5e50b2468 100644
--- a/torchao/prototype/mx_formats/mx_tensor.py
+++ b/torchao/prototype/mx_formats/mx_tensor.py
@@ -362,6 +362,7 @@ def to_dtype(
     # unpacking and unscaling
     if is_transposed:
         data_lp = data_lp.t()
+        scale_e8m0 = scale_e8m0.t()
         assert data_lp.is_contiguous()
         orig_shape = (orig_shape[1], orig_shape[0])
 
@@ -688,7 +689,7 @@ def _addmm_mx_dispatch(
         assert b._block_size == 32, f"Invalid block size {b._block_size}"
 
         a_scale = a.scale.view(M, K // a._block_size)
-        b_scale = b.scale.view(N, K // b._block_size)
+        b_scale = b.scale.t().view(N, K // b._block_size)
         a_scale_block = to_blocked(a_scale)
         b_scale_block = to_blocked(b_scale)
 
@@ -759,7 +760,7 @@ def mx_t(func, types, args, kwargs):
     old = args[0]
     new = MXTensor(
         old.qdata.t(),
-        old.scale,
+        old.scale.t(),
         old._elem_dtype,
         old._block_size,
         old._orig_dtype,