diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py
index 914dc68469ee..5a8f51de6462 100644
--- a/vllm/model_executor/layers/fused_moe/layer.py
+++ b/vllm/model_executor/layers/fused_moe/layer.py
@@ -700,7 +700,7 @@ def layer_id(self):
 
     @property
     def gate(self) -> torch.nn.Module | None:
-        return self._gate
+        return self._gate if self.use_overlapped else None
 
     @property
     def tp_size(self):
@@ -725,7 +725,7 @@ def use_ep(self):
     @property
     def is_internal_router(self) -> bool:
         # By default, router/gate is called before FusedMoE forward pass
-        return self._gate is not None
+        return self.gate is not None
 
     def _maybe_init_expert_routing_tables(
         self,
@@ -1457,7 +1457,6 @@ def forward_native(
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        self.ensure_moe_quant_config_init()
         return self.runner.forward(
             hidden_states,
             router_logits,
diff --git a/vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py b/vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py
index 12b795f30e22..b265cbb41211 100644
--- a/vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py
+++ b/vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py
@@ -63,6 +63,8 @@ def _moe_forward(
     layer_name: str,
 ) -> torch.Tensor:
     layer = get_layer_from_name(layer_name)
+    # TODO(bnell): this can be removed after MK migration is complete.
+    layer.ensure_moe_quant_config_init()
     return layer.runner.forward_impl(
         layer, hidden_states, router_logits, shared_experts_input
     )
@@ -84,6 +86,8 @@ def _moe_forward_shared(
     layer_name: str,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     layer = get_layer_from_name(layer_name)
+    # TODO(bnell): this can be removed after MK migration is complete.
+    layer.ensure_moe_quant_config_init()
     return layer.runner.forward_impl(
         layer, hidden_states, router_logits, shared_experts_input
     )