diff --git a/patches/test_nvfp4_mega_moe.py b/patches/test_nvfp4_mega_moe.py
index da1d19c..b956aaa 100644
--- a/patches/test_nvfp4_mega_moe.py
+++ b/patches/test_nvfp4_mega_moe.py
@@ -82,9 +82,16 @@ def test_nvfp4_mega_moe():
     symm_buffer.x[:num_tokens].copy_(
         torch.randint(0, 256, (num_tokens, hidden // 2), dtype=torch.uint8, device=device).view(torch.int8))
     # Write valid UE4M3 scales (random but non-zero)
-    sf_data = torch.randn(num_tokens, hidden // 64, dtype=torch.float8_e4m3fn, device=device).abs().clamp(0.1, 5.0)
-    symm_buffer.x_sf[:num_tokens].copy_(
-        sf_data.view(torch.uint8).reshape(num_tokens, hidden // 64).contiguous().view(torch.int32))
+    # Can't randn with float8, so generate in bf16 then cast
+    sf_bf16 = torch.randn(num_tokens, hidden // 64, dtype=torch.bfloat16, device=device).abs().clamp(0.1, 5.0)
+    # Pack as int32 (4 UE4M3 bytes per int32)
+    sf_u8 = sf_bf16.to(torch.float8_e4m3fn).view(torch.uint8)
+    # Pack 4 uint8 into int32
+    sf_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))
+    symm_buffer.x_sf[:num_tokens].copy_(sf_packed)
     # Write topk data directly
     for i in range(num_tokens):
         for j in range(top_k):