diff --git a/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py b/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
index 3e6a2e35..6f3bb07d 100644
--- a/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
+++ b/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
@@ -401,33 +401,39 @@ def nvfp4_mega_moe_full(
             x_u8 = x_fp4[s0].view(torch.uint8)
             lo = (x_u8 & 0x0F).long()
             hi = ((x_u8 >> 4) & 0x0F).long()
-            x_nib = torch.stack([lo, hi], dim=-1).reshape(-1)
+            x_nib = torch.stack([lo, hi], dim=-1).reshape(-1)  # (K,) — 1D so simple flatten works
             x_signs = (x_nib >> 3).float() * -2 + 1
             x_mags = _E2M1_MAGNITUDES.to(device=x_u8.device)[(x_nib & 0x07)]
-            x_deq = x_signs * x_mags
-            sf_exp = x_sf[s0].to(torch.float32).repeat_interleave(16, dim=-1)
+            x_deq = x_signs * x_mags  # (K,) = (7168,)
+            sf_exp = x_sf[s0].to(torch.float32).repeat_interleave(16, dim=-1)  # (K,)
             igs = float(l1_global_scale) if not isinstance(l1_global_scale, float) else l1_global_scale
             x_bf16 = (x_deq * sf_exp * igs).to(torch.bfloat16)
             # Dequantize L1 weight for expert e0
             w_u8 = l1_w[e0].view(torch.uint8)
             wlo = (w_u8 & 0x0F).long()
             whi = ((w_u8 >> 4) & 0x0F).long()
-            w_nib = torch.stack([wlo, whi], dim=-1).reshape(w_u8.shape[0], -1)
+            w_nib = torch.stack([wlo, whi], dim=-1).reshape(w_u8.shape[0] * 2, w_u8.shape[1])  # (K, N)
             w_signs = (w_nib >> 3).float() * -2 + 1
             w_mags = _E2M1_MAGNITUDES.to(device=w_u8.device)[(w_nib & 0x07)]
-            w_deq = w_signs * w_mags
-            w_sf_exp = l1_sf[e0].to(torch.float32).repeat_interleave(16, dim=0)
+            w_deq = w_signs * w_mags  # (K, N) = (7168, 6144)
+            # Weight SF: (sf_k, N) = (448, 6144). Each SF covers 16 FP4 values (8 bytes).
+            # repeat_interleave(16) on dim 0 gives (7168, 6144) — but wait,
+            # sf_k=448, and K=7168, so 448*16=7168. This is per-FP4-value already.
+            w_sf_exp = l1_sf[e0].to(torch.float32).repeat_interleave(16, dim=0)  # (K, N)
             gs = l1_global_sf[e0]
             if gs.dim() == 0:
                 w_bf16 = (w_deq * w_sf_exp * gs.item()).to(torch.bfloat16)
             else:
                 w_bf16 = (w_deq * w_sf_exp).to(torch.bfloat16)
+            # GEMM computes: (w * w_sf) @ (x * x_sf) * alpha * gs
+            # We compute: (x * x_sf * igs) @ (w * w_sf * gs_per_half) 
+            # which equals igs * gs * (x_sf * w_sf) @ (x * w) = same as GEMM
             ref_out = torch.nn.functional.linear(x_bf16.unsqueeze(0), w_bf16.T).squeeze(0)
             if gs.dim() > 0:
                 gn = ref_out.shape[0] // 2
                 ref_out[:gn] = ref_out[:gn] * gs[0].item()
                 ref_out[gn:] = ref_out[gn:] * gs[1].item()
-            ref_out = ref_out * igs
+            # DON'T multiply by igs again — already in x_bf16
             nvfp4_mega_moe_full._ref_l1 = (s0, e0, ref_out)
             print(f"[BF16-REF-L1] expert={e0} amax={ref_out.abs().max():.4e} mean={ref_out.mean():.4e}")
         except Exception as ex: