diff --git a/single_shot_inference.py b/single_shot_inference.py
index 5e00618f..71afc298 100644
--- a/single_shot_inference.py
+++ b/single_shot_inference.py
@@ -322,15 +322,17 @@ 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
-            gsb = 1.0 * ws2_v
+            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)
             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
-            gsb = 1.0 * ws2_v
+            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)
-            gate_bf16 = dequantize_nvfp4(gate_w.to(dev), gate_ws.to(dev), gsa)  # (out, in)
+            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")
         self.kv_norm_w = w.get(f"{pfx}.kv_norm.weight")
@@ -420,7 +422,8 @@ class Indexer:
         if wp_w is not None:
             from dsv4.ops.quantize import dequantize_nvfp4
             ws2_v = wp_ws2.float().item() if wp_ws2 is not None else 1.0
-            gsb = 1.0 * ws2_v
+            isc_v = wp_isc.float().item() if wp_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] * wp_w.shape[0], device=dev, dtype=torch.float32)
             wp_bf16 = dequantize_nvfp4(wp_w.to(dev), wp_ws.to(dev), gsa)
             self._wp_bf16 = wp_bf16.to(dev).contiguous()
@@ -1323,7 +1326,7 @@ 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 = 1.0 * ws2_v  # global_scale_b = gs * ws2
+                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)
                 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)