diff --git a/single_shot_inference.py b/single_shot_inference.py
index 71afc298..9bf945ca 100644
--- a/single_shot_inference.py
+++ b/single_shot_inference.py
@@ -322,16 +322,14 @@ class Compressor:
         gate_w, gate_ws, gate_ws2, gate_isc = get_nvfp4_weight(w, pfx, 'gate_proj')
         if kv_w is not None:
             ws2_v = kv_ws2.float().item() if kv_ws2 is not None else 1.0
-            isc_v = kv_isc.float().item() if kv_isc is not None else 1.0/(6.0*448.0)
-            gsb = isc_v * ws2_v  # global_scale_b = input_scale * weight_scale_2
-            gsa = torch.tensor([gsb] * kv_w.shape[0], device=dev, dtype=torch.float32)
+            # For weight dequantization: gsa = ws2 (NOT input_scale * ws2)
+            # input_scale is the activation global scale, only used in GEMM's gsb computation
+            gsa = torch.tensor([ws2_v] * kv_w.shape[0], device=dev, dtype=torch.float32)
             kv_bf16 = dequantize_nvfp4(kv_w.to(dev), kv_ws.to(dev), gsa)  # (out, in)
             self._kv_bf16 = kv_bf16.to(dev).contiguous()
         if gate_w is not None:
             ws2_v = gate_ws2.float().item() if gate_ws2 is not None else 1.0
-            isc_v = gate_isc.float().item() if gate_isc is not None else 1.0/(6.0*448.0)
-            gsb = isc_v * ws2_v
-            gsa = torch.tensor([gsb] * gate_w.shape[0], device=dev, dtype=torch.float32)
+            gsa = torch.tensor([ws2_v] * gate_w.shape[0], device=dev, dtype=torch.float32)
             gate_bf16 = dequantize_nvfp4(gate_w.to(dev), gate_ws.to(dev), gsa)
             self._gate_bf16 = gate_bf16.to(dev).contiguous()
         self.ape = w.get(f"{pfx}.position_bias")
@@ -1326,8 +1324,8 @@ def main():
                 # Checkpoint has NVFP4 gate weight — dequantize to BF16
                 from dsv4.ops.quantize import dequantize_nvfp4
                 ws2_v = gate_ws2.float().item() if gate_ws2 is not None else 1.0
-                gsb = isc_v * ws2_v  # global_scale_b = input_scale * weight_scale_2
-                gsa = torch.tensor([gsb] * gate_w.shape[0], device=dev, dtype=torch.float32)
+                # For weight dequantization: gsa = ws2 (NOT input_scale * ws2)
+                gsa = torch.tensor([ws2_v] * gate_w.shape[0], device=dev, dtype=torch.float32)
                 gate_bf16 = dequantize_nvfp4(gate_w.to(dev), gate_ws.to(dev), gsa)  # (E_packed*2, H)
                 router.W_gate = gate_bf16.T.contiguous().to(dev)  # (H, E) for F.linear(x, W_gate.T)
             else: