diff --git a/single_shot_inference.py b/single_shot_inference.py
index 869ff861..a1ce5512 100644
--- a/single_shot_inference.py
+++ b/single_shot_inference.py
@@ -789,13 +789,21 @@ def main():
     embed_w = all_w.get("model.embed_tokens.weight")
     embed = torch.nn.Embedding.from_pretrained(embed_w.bfloat16().to('cuda:0'))
     # lm_head: quantize to NVFP4 for tensor-core acceleration
+    # Weight is (vocab_size, hidden_size) = (N, K) in BF16
+    # quantize_weight_to_nvfp4 expects (K, N), so transpose first
+    # But Nvfp4Linear expects (N_packed, K_packed) from checkpoint layout
+    # quantize_weight_to_nvfp4 returns (K//2, N) which IS (K_packed, N)
+    # So we need to transpose the weight, quantize as (K, N),
+    # then the result (K//2, N) needs to be transposed to (N, K//2) for Nvfp4Linear.
     lm_w_raw = all_w.get("lm_head.weight", embed_w).bfloat16().to('cuda:0')
     from dsv4.layers.linear import Nvfp4Linear
     lm_head_lin = Nvfp4Linear(lm_w_raw.shape[1], lm_w_raw.shape[0], max_num_tokens=8192, device='cuda:0')
-    from dsv4.ops.quantize import quantize_to_nvfp4
-    lm_fp4, lm_sf, lm_gs = quantize_to_nvfp4(lm_w_raw.T.contiguous())  # (K, N) for quantize
-    lm_head_lin.fp4 = [lm_fp4]
-    lm_head_lin.sf = [lm_sf]
+    from dsv4.ops.quantize import quantize_weight_to_nvfp4
+    # quantize_weight_to_nvfp4 takes (K, N) → returns (K//2, N), (K//16, N), gs
+    lm_fp4, lm_sf, lm_gs = quantize_weight_to_nvfp4(lm_w_raw.T.contiguous())  # (K//2, N) = (3584, 128K)
+    # Nvfp4Linear expects fp4 in (N_packed, K_packed) layout, so transpose
+    lm_head_lin.fp4 = [lm_fp4.permute(1, 0).contiguous()]  # (N, K_packed) = (128K, 3584)
+    lm_head_lin.sf = [lm_sf.permute(1, 0).contiguous()]  # (N, K_sf) = (128K, 448)
     lm_head_lin.gs = [lm_gs]  # global scale from weight quantization
     lm_head_lin.ws2 = [None]  # no separate weight_scale_2
     lm_head_lin._activation_global_scale = 1.0 / (6.0 * 448.0)  # placeholder