diff --git a/mlx_lm/models/nemotron_h.py b/mlx_lm/models/nemotron_h.py
index 353de36c9..3d4636253 100644
--- a/mlx_lm/models/nemotron_h.py
+++ b/mlx_lm/models/nemotron_h.py
@@ -357,6 +357,11 @@ def __init__(self, config: ModelArgs):
         self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))
         self.e_score_correction_bias = mx.zeros((self.n_routed_experts,))
 
+    def to_quantized(self, group_size: int = 64, bits: int = 4, mode: str = "affine"):
+        # Router gate weights are tiny and path-dependent; keep them unquantized
+        # so per-path quantization configs can reference the gate without failing.
+        return self
+
     def __call__(self, x):
         return group_expert_select(
             x @ self.weight.T,
diff --git a/tests/test_models.py b/tests/test_models.py
index 6e1fcd96e..ac971cc3b 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -842,6 +842,57 @@ def test_gemma4_moe_router_quantizes_to_8bit(self):
         )
         self.assertEqual(config["quantization"]["bits"], 4)
 
+    def test_nemotron_h_moe_gate_quantization(self):
+        from mlx_lm.models import nemotron_h
+
+        args = nemotron_h.ModelArgs.from_dict(
+            {
+                "model_type": "nemotron_h",
+                "vocab_size": 128,
+                "hidden_size": 64,
+                "intermediate_size": 128,
+                "num_hidden_layers": 1,
+                "max_position_embeddings": 128,
+                "num_attention_heads": 4,
+                "num_key_value_heads": 2,
+                "attention_bias": False,
+                "mamba_num_heads": 4,
+                "mamba_head_dim": 16,
+                "mamba_proj_bias": False,
+                "ssm_state_size": 16,
+                "conv_kernel": 3,
+                "n_groups": 1,
+                "mlp_bias": False,
+                "layer_norm_epsilon": 1e-5,
+                "use_bias": False,
+                "use_conv_bias": True,
+                "hybrid_override_pattern": ["E"],
+                "moe_intermediate_size": 64,
+                "n_routed_experts": 4,
+                "n_group": 1,
+                "topk_group": 1,
+                "num_experts_per_tok": 2,
+                "norm_topk_prob": True,
+                "routed_scaling_factor": 1.0,
+            }
+        )
+        model = nemotron_h.Model(args)
+        gate_path = "backbone.layers.0.mixer.gate"
+
+        def class_predicate(path, module):
+            # Mirror utils.py: a per-path quantization config entry yields a
+            # truthy result even for modules that cannot themselves be quantized.
+            if path == gate_path:
+                return {"group_size": 64, "bits": 4}
+            return hasattr(module, "to_quantized")
+
+        # Must not raise: MoEGate exposes a no-op to_quantized().
+        nn.quantize(model, group_size=64, bits=4, class_predicate=class_predicate)
+
+        gate = model.backbone.layers[0].mixer.gate
+        self.assertIsInstance(gate, nemotron_h.MoEGate)
+        self.assertIs(gate.to_quantized(), gate)
+
     def test_qwen2_moe(self):
         from mlx_lm.models import qwen2_moe