diff --git a/modelopt/torch/quantization/plugins/huggingface.py b/modelopt/torch/quantization/plugins/huggingface.py
index 97e13f419f9..a888f50319a 100644
--- a/modelopt/torch/quantization/plugins/huggingface.py
+++ b/modelopt/torch/quantization/plugins/huggingface.py
@@ -1471,7 +1471,8 @@ def _is_fused_experts_module(module):
 
     Detects the standardized HuggingFace transformers 5.0+ fused expert pattern:
     ``gate_up_proj`` (3-D parameter), ``down_proj`` (3-D parameter), ``num_experts``,
-    and ``act_fn``.  Matches ``MixtralExperts``, ``Qwen2MoeExperts``,
+    and ``act_fn`` (or ``_apply_gate`` for clamped-swiglu experts such as
+    ``MiniMaxM3VLExperts``).  Matches ``MixtralExperts``, ``Qwen2MoeExperts``,
     ``Qwen3MoeExperts``, ``Qwen3_5MoeExperts``, ``DeepseekV3NaiveMoe``,
     ``JambaExperts``, ``OlmoeExperts``, etc.
 
@@ -1480,7 +1481,9 @@ def _is_fused_experts_module(module):
     """
     if not hasattr(module, "gate_up_proj") or not hasattr(module, "down_proj"):
         return False
-    if not hasattr(module, "num_experts") or not hasattr(module, "act_fn"):
+    if not hasattr(module, "num_experts") or not (
+        hasattr(module, "act_fn") or hasattr(module, "_apply_gate")
+    ):
         return False
     gate_up = getattr(module, "gate_up_proj")
     down = getattr(module, "down_proj")
diff --git a/tests/unit/torch/quantization/plugins/test_fused_experts.py b/tests/unit/torch/quantization/plugins/test_fused_experts.py
index ce23f7a51d5..ff9fd48fa26 100644
--- a/tests/unit/torch/quantization/plugins/test_fused_experts.py
+++ b/tests/unit/torch/quantization/plugins/test_fused_experts.py
@@ -152,6 +152,21 @@ def test_module_missing_act_fn_not_detected(self):
         module.num_experts = 4
         assert _is_fused_experts_module(module) is False
 
+    def test_module_with_apply_gate_detected(self):
+        """Clamped-swiglu experts (e.g. MiniMaxM3VLExperts) use _apply_gate instead of act_fn."""
+
+        class _ApplyGateExperts(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.gate_up_proj = nn.Parameter(torch.randn(4, 16, 8))
+                self.down_proj = nn.Parameter(torch.randn(4, 8, 16))
+                self.num_experts = 4
+
+            def _apply_gate(self, gate, up):
+                return up * torch.sigmoid(gate)
+
+        assert _is_fused_experts_module(_ApplyGateExperts()) is True
+
     def test_sparse_moe_block_not_detected_as_fused(self):
         block = _SyntheticSparseMoeBlock()
         assert _is_fused_experts_module(block) is False