diff --git a/modelopt/torch/export/quant_utils.py b/modelopt/torch/export/quant_utils.py
index 4ceb51cd2c..1202eeac39 100755
--- a/modelopt/torch/export/quant_utils.py
+++ b/modelopt/torch/export/quant_utils.py
@@ -1375,11 +1375,21 @@ def preprocess_linear_fusion(modules: list[torch.nn.Module], resmooth_only=False
             return
 
         if modules[0].input_quantizer.is_enabled and modules[0].input_quantizer.amax is not None:
-            assert modules[0].input_quantizer.amax.numel() == 1, (
-                "Only support scalar input quant amax"
-            )
-
-            input_amax = torch.max(torch.stack([module.input_quantizer.amax for module in modules]))
+            if modules[0].input_quantizer.amax.numel() == 1:
+                # Scalar amax (e.g. dense layers with per-tensor activation quant):
+                # unify via scalar max across the modules being fused.
+                input_amax = torch.max(
+                    torch.stack([module.input_quantizer.amax for module in modules])
+                )
+            else:
+                # Non-scalar amax (e.g. NVFP4 per-channel input quantizer on
+                # per-expert-decomposed MoE). Modules being fused here share the
+                # same input tensor, so their per-channel amax vectors are
+                # identical by construction. Elementwise max is a no-op in that
+                # case and is the correct unification rule if they ever differ.
+                input_amax = torch.stack(
+                    [module.input_quantizer.amax for module in modules]
+                ).amax(dim=0)
             for module in modules:
                 module.input_quantizer.amax = input_amax