diff --git a/modelopt/torch/quantization/model_calib.py b/modelopt/torch/quantization/model_calib.py
index 0077d8666..61559ea41 100644
--- a/modelopt/torch/quantization/model_calib.py
+++ b/modelopt/torch/quantization/model_calib.py
@@ -62,6 +62,34 @@ def weight_only_quantize(model: nn.Module):
         seen_modules.add(module)
 
 
+def _has_expert_parallelism(module: nn.Module) -> bool:
+    """Check if module has expert parallelism enabled."""
+    ps = getattr(module, "parallel_state", None)
+    return ps is not None and ps.expert_model_parallel_group.is_initialized()
+
+
+def _check_moe_calibration_complete(quantizer, parallel_state):
+    """Raise error if MoE calibration is incomplete (some ranks have amax, others don't)."""
+    if isinstance(quantizer, SequentialQuantizer):
+        for _q in quantizer:
+            _check_moe_calibration_complete(_q, parallel_state)
+        return
+    for group in [
+        parallel_state.data_parallel_group,
+        parallel_state.expert_model_parallel_group,
+        parallel_state.tensor_parallel_group,
+    ]:
+        if not group.is_initialized():
+            continue
+        has_amax = getattr(quantizer, "_amax", None) is not None
+        amax_states = DistributedProcessGroup.get_dist_syncd_obj(has_amax, group, lambda objs: objs)
+        if any(amax_states) and not all(amax_states):
+            raise RuntimeError(
+                "MoE calibration incomplete: some experts received no tokens during calibration. "
+                "Increase --calib-size to ensure all experts see calibration data."
+            )
+
+
 @torch.no_grad()
 def max_calibrate(model: nn.Module, forward_loop: ForwardLoop | None = None, distributed_sync=True):
     """Calibrate the model using max.
@@ -81,9 +109,21 @@ def max_calibrate(model: nn.Module, forward_loop: ForwardLoop | None = None, dis
         forward_loop(model)
     finish_stats_collection(model)
 
+    # Sync amax across local experts within each rank (for SequentialMLP)
+    for name, module in model.named_modules():
+        if hasattr(module, "sync_moe_local_experts_amax"):
+            module.sync_moe_local_experts_amax()
+
     if not distributed_sync:
         return
 
+    # Check MoE calibration completeness before sync
+    for name, module in model.named_modules():
+        if isinstance(module, QuantModule) and _has_expert_parallelism(module):
+            for child in module.children():
+                if isinstance(child, (TensorQuantizer, SequentialQuantizer)):
+                    _check_moe_calibration_complete(child, module.parallel_state)
+
     def sync_quantizer_amax_across_dp_ep(quantizer, parallel_state):
         """Synchronize the amax across all ranks in the data parallel and expert parallel groups."""
         if isinstance(quantizer, SequentialQuantizer):
@@ -182,10 +222,6 @@ def sync_quantizer_amax_across_tp(
                 parallel_state=module.parallel_state,
             )
 
-        # MOE Quantization
-        if hasattr(module, "sync_moe_local_experts_amax"):
-            module.sync_moe_local_experts_amax()
-
         # KV Cache Quantization
         if hasattr(module, "k_bmm_quantizer") and hasattr(module, "v_bmm_quantizer"):
             # We only support KVCache quantization with scalar per-tensor states for now (NVFP4 & FP8 KV cache)
diff --git a/modelopt/torch/quantization/plugins/megatron.py b/modelopt/torch/quantization/plugins/megatron.py
index 6a2bdc15f..9d31f848b 100644
--- a/modelopt/torch/quantization/plugins/megatron.py
+++ b/modelopt/torch/quantization/plugins/megatron.py
@@ -24,7 +24,6 @@
 import megatron.core.tensor_parallel.layers as megatron_parallel
 import megatron.core.transformer.mlp as megatron_mlp
 import megatron.core.transformer.moe.experts as megatron_moe
-import megatron.core.transformer.moe.moe_layer as megatron_moe_layer
 import torch
 from megatron.core.parallel_state import get_data_parallel_group
 from megatron.core.tensor_parallel.mappings import gather_from_sequence_parallel_region
@@ -735,35 +734,3 @@ def sharded_state_dict(self, prefix="", sharded_offsets=(), metadata=None):
             # Affine KVCache Quant bias vector.
             state_dict = self.state_dict(prefix="", keep_vars=True)
             return make_sharded_tensors_for_checkpoint(state_dict, prefix, {}, sharded_offsets)
-
-
-@QuantModuleRegistry.register({megatron_moe_layer.MoELayer: "megatron_moe_MoELayer"})
-class _QuantMoELayer(QuantModule):
-    """Module to support special handling of token dispatching during calibration.
-
-    During calibration, we forward all tokens to all experts so that all experts see sufficient tokens to calibrate.
-    However, even in calibration mode, the actual top_k routing is used to calculate the actual outputs this instance
-    returns.
-
-    When group routing is used (e.g. DSR1 or V3), with group_topk set, topk can never be all experts (otherwise,
-    the total indices will be out of bound). Since DSR1 is the only model that uses group routing and all experts
-    can be calibrated normally, we disable this WAR when group_topk is used.
-
-    If calibration is not enabled, this module behaves as a normal MoELayer.
-
-    Note:
-        There are new arguments (e.g. padding_mask) passed through the forward function. Since we always pass through
-        all the arguments, we use **args and **kwargs here.
-    """
-
-    def _setup(self):
-        pass
-
-    def forward(self, *args, **kwargs):
-        if any(getattr(m, "_if_calib", False) for m in self.experts.modules()):
-            if self.config.moe_router_num_groups is None:
-                original_top_k = self.router.topk
-                self.router.topk = self.router.num_experts
-                super().forward(*args, **kwargs)
-                self.router.topk = original_top_k
-        return super().forward(*args, **kwargs)