diff --git a/single_shot_inference.py b/single_shot_inference.py
index 2b4cd9be..4f87c086 100644
--- a/single_shot_inference.py
+++ b/single_shot_inference.py
@@ -454,7 +454,8 @@ def _load_moe_weights_stacked(all_w, li, pfx, dev, moe, cfg):
             l1_fp4_list.append(torch.cat([gw, uw], dim=0).to(dev))
             if gws is not None and uws is not None: l1_sf_list.append(torch.cat([gws, uws], dim=0).to(dev))
             gs = gisc.float().item() if gisc is not None else 1.0 / (6.0 * 448.0)
-            l1_gs_list.append(gs)
+            l1_gs_list.append(1.0)  # gsb base — ws2 will be folded in by _ensure_stacked
+            l1_gsa_list.append(gs)  # gsa = input_scale
             # weight_scale_2: scalar, folded into global_scale_b
             l1_ws2_list.append(gws2.to(dev) if gws2 is not None else None)
         dw, dws, dws2, disc = get_nvfp4_weight(all_w, ep, 'down_proj')
@@ -462,7 +463,8 @@ def _load_moe_weights_stacked(all_w, li, pfx, dev, moe, cfg):
             l2_fp4_list.append(dw.to(dev))
             if dws is not None: l2_sf_list.append(dws.to(dev))
             gs2 = disc.float().item() if disc is not None else 1.0 / (6.0 * 448.0)
-            l2_gs_list.append(gs2)
+            l2_gs_list.append(1.0)  # gsb base
+            l2_gsa_list.append(gs2)  # gsa = input_scale
             l2_ws2_list.append(dws2.to(dev) if dws2 is not None else None)
     if not l1_fp4_list: log.warning(f"L{li}: No expert weights found"); return
     l1_stacked = torch.stack(l1_fp4_list).to(dev)
@@ -471,6 +473,11 @@ def _load_moe_weights_stacked(all_w, li, pfx, dev, moe, cfg):
     l2_sf_stacked = torch.stack(l2_sf_list).to(dev) if l2_sf_list else None
     del l1_fp4_list, l1_sf_list, l2_fp4_list, l2_sf_list
     moe.prepare_weights_from_stacked(l1_stacked, l1_sf_stacked, l1_gs_list, l2_stacked, l2_sf_stacked, l2_gs_list)
+    # Set activation global scales from input_scale (gsa = input_scale, gsb = weight_scale_2)
+    if l1_gsa_list:
+        moe._l1_activation_global_scale = l1_gsa_list[0]  # Use first expert's input_scale
+    if l2_gsa_list:
+        moe._l2_activation_global_scale = l2_gsa_list[0]  # Use first expert's input_scale
     moe.l1_ws2 = l1_ws2_list
     moe.l2_ws2 = l2_ws2_list
 
@@ -481,13 +488,17 @@ def _load_shared_expert_weights(all_w, li, pfx, dev, se, cfg):
     if gw is not None and uw is not None:
         se.l1_fp4 = [torch.cat([gw, uw], dim=0).to(dev)]
         se.l1_sf = [torch.cat([gws, uws], dim=0).to(dev)] if gws is not None and uws is not None else [torch.zeros(1, device=dev, dtype=torch.float8_e4m3fn)]
-        se.l1_gs = [gisc.float().item() if gisc is not None else 1.0 / (6.0 * 448.0)]
-        se.l1_ws2 = [gws2.to(dev) if gws2 is not None else None]  # weight_scale_2
+        l1_isc = gisc.float().item() if gisc is not None else 1.0 / (6.0 * 448.0)
+        se.l1_gs = [1.0]  # gsb base — ws2 will be folded in by finalize_weights
+        se.l1_ws2 = [gws2.to(dev) if gws2 is not None else None]
+        se._l1_activation_global_scale = l1_isc  # gsa = input_scale
     if dw is not None:
         se.l2_fp4 = [dw.to(dev)]
         se.l2_sf = [dws.to(dev)] if dws is not None else [torch.zeros(1, device=dev, dtype=torch.float8_e4m3fn)]
-        se.l2_gs = [disc.float().item() if disc is not None else 1.0 / (6.0 * 448.0)]
-        se.l2_ws2 = [dws2.to(dev) if dws2 is not None else None]  # weight_scale_2
+        l2_isc = disc.float().item() if disc is not None else 1.0 / (6.0 * 448.0)
+        se.l2_gs = [1.0]  # gsb base
+        se.l2_ws2 = [dws2.to(dev) if dws2 is not None else None]
+        se._l2_activation_global_scale = l2_isc  # gsa = input_scale
 
 def _cache_layer_weights_no_experts(all_w, n_layers, devices):
     cached = {}