diff --git a/deep_gemm/mega/__init__.py b/deep_gemm/mega/__init__.py
index ed519b2..f4cd0f4 100644
--- a/deep_gemm/mega/__init__.py
+++ b/deep_gemm/mega/__init__.py
@@ -163,23 +163,41 @@ def transform_nvfp4_weights_for_mega_moe(
     l2_sf = fold_global_scale(l2_weights[1], l2_weight_scale_2)
     
     num_experts = l1_weights[0].shape[0]
-    l1_n = l1_weights[0].shape[1]  # intermediate_size * 2
-    l1_k = l1_weights[0].shape[2] * 2  # K (weight is K//2 uint8)
+    l1_n = l1_weights[0].shape[1]
+    l1_k = l1_weights[0].shape[2] * 2
     l2_n = l2_weights[0].shape[1]
     l2_k = l2_weights[0].shape[2] * 2
     
+    # Pack UE4M3 (float8_e4m3fn) into int32 for DeepGEMM TMA consumption
+    # 4 UE4M3 bytes → 1 int32, matching the hardware's 4X scale vector
+    def pack_ue4m3_to_int32(sf):
+        sf_u8 = sf.view(torch.uint8)
+        # Pack 4 consecutive uint8 bytes into int32
+        assert sf_u8.shape[-1] % 4 == 0
+        packed = (sf_u8[..., 0::4].to(torch.int32) |
+                  (sf_u8[..., 1::4].to(torch.int32) << 8) |
+                  (sf_u8[..., 2::4].to(torch.int32) << 16) |
+                  (sf_u8[..., 3::4].to(torch.int32) << 24))
+        return packed.contiguous()
+    
+    l1_sf_packed = pack_ue4m3_to_int32(l1_sf)
+    l2_sf_packed = pack_ue4m3_to_int32(l2_sf)
+    
     # Transform SF into TMA-aligned UTCCP layout using DeepGEMM's C++ function
-    # recipe (1, 1, 16): gran_mn=1, gran_k=16
-    l1_sf_packed = transform_sf_into_required_layout(
-        l1_sf, l1_n, l1_k, (1, 16), num_experts)
-    l2_sf_packed = transform_sf_into_required_layout(
-        l2_sf, l2_n, l2_k, (1, 16), num_experts)
+    # Pass as kInt with recipe (1, 16): gran_mn=1, gran_k=16
+    # After packing, effective K for SF is k/4 (4 UE4M3 per int32)
+    # check_sf_layout expects: sf.size(-1) = ceil_div(k, gran_k * 4) = ceil_div(k, 64)
+    # Our packed shape is (experts, mn, K/64) — matches!
+    l1_sf_transformed = transform_sf_into_required_layout(
+        l1_sf_packed, l1_n, l1_k, (1, 16), num_experts)
+    l2_sf_transformed = transform_sf_into_required_layout(
+        l2_sf_packed, l2_n, l2_k, (1, 16), num_experts)
     
     # L1: interleave gate/up
     l1_interleaved = _interleave_l1_weights((l1_weights[0], l1_sf_packed))
     # 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_packed)
+    l1_out = (l1_interleaved[0].view(torch.int8), l1_sf_transformed)
+    l2_out = (l2_weights[0].view(torch.int8), l2_sf_transformed)
     return l1_out, l2_out