diff --git a/deep_gemm/mega/__init__.py b/deep_gemm/mega/__init__.py
index 206c6cb..f2afc50 100644
--- a/deep_gemm/mega/__init__.py
+++ b/deep_gemm/mega/__init__.py
@@ -146,17 +146,21 @@ def _interleave_l1_weights(l1_weights: Tuple[torch.Tensor, torch.Tensor]) -> Tup
         return torch.empty_like(t).copy_(torch.stack([gate, up], dim=2).reshape(g, n, *rest))
 
     def interleave_sf_mn_major(t, gran: int = 8) -> torch.Tensor:
-        """Interleave SF while preserving MN-major layout (stride(-2)=1, stride(-1)=TMA-aligned)."""
-        # First do the interleave in K-major, then transpose back to MN-major
-        # t is MN-major: (num_groups, mn, packed_sf_k) with stride(-2)=1
-        t_k_major = t.transpose(-2, -1).contiguous()  # (num_groups, packed_sf_k, mn) C-contiguous
-        g, k, mn = t_k_major.shape
+        """Interleave SF while preserving MN-major layout (stride(-2)=1, stride(-1)=TMA-aligned).
+
+        Input/Output shape: (num_groups, mn, packed_sf_k) with MN-major strides.
+        Interleaves the mn dimension: [gate_0..7, up_0..7, gate_8..15, up_8..15, ...]
+        """
+        # t: (groups, mn, packed_sf_k) MN-major, stride(-2)=1
+        # Step 1: transpose to K-major so we can use C-contiguous ops
+        t_k = t.transpose(-2, -1).contiguous()  # (groups, packed_sf_k, mn) C-contiguous
+        g, k, mn = t_k.shape
         half = mn // 2
-        gate = t_k_major[:, :, :half].reshape(g, k, half // gran, gran)
-        up = t_k_major[:, :, half:].reshape(g, k, half // gran, gran)
-        interleaved_k = torch.empty_like(t_k_major).copy_(
-            torch.stack([gate, up], dim=3).reshape(g, k, mn))
-        # Transpose back to MN-major
+        gate = t_k[:, :, :half].reshape(g, k, half // gran, gran)
+        up = t_k[:, :, half:].reshape(g, k, half // gran, gran)
+        interleaved_k = torch.empty(g, k, mn, dtype=t.dtype, device=t.device)
+        interleaved_k.copy_(torch.stack([gate, up], dim=3).reshape(g, k, mn))
+        # Step 2: transpose back to MN-major
         return interleaved_k.transpose(-2, -1).contiguous().transpose(-2, -1)
 
     return interleave(l1_weights[0]), interleave_sf_mn_major(l1_weights[1])
@@ -288,6 +292,8 @@ def transform_nvfp4_weights_for_mega_moe(
     # DeepGEMM expects int8 (kPackedFP4 = torch.kInt8)
     l1_out = (l1_interleaved[0].view(torch.int8), l1_interleaved[1])
     l2_out = (l2_weights[0].view(torch.int8), l2_sf_transformed)
+    print(f"[debug-transform] l1_out_sf: dtype={l1_out[1].dtype} shape={tuple(l1_out[1].shape)} strides={l1_out[1].stride()}", flush=True)
+    print(f"[debug-transform] l2_out_sf: dtype={l2_out[1].dtype} shape={tuple(l2_out[1].shape)} strides={l2_out[1].stride()}", flush=True)
     return l1_out, l2_out