diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py
index a80e0e78f..859f1c8b7 100644
--- a/python/sglang/srt/configs/model_config.py
+++ b/python/sglang/srt/configs/model_config.py
@@ -665,7 +665,10 @@ class ModelConfig:
             quant_cfg = quant_cfg.to_dict()
         if quant_cfg is not None:
             # Identify modelopt quantization
-            if "quant_method" not in quant_cfg:
+            if (
+                "quant_method" not in quant_cfg
+                or quant_cfg["quant_method"] == "modelopt"
+            ):
                 parsed_cfg = self._parse_modelopt_quant_config(
                     {"quantization": quant_cfg}
                 )
diff --git a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
index da7157c8a..f7b886b39 100644
--- a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
+++ b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
@@ -418,7 +418,9 @@ class FusedMoE(torch.nn.Module):
         # w3, up_proj: Load into second logical weight of w13.
         # trtllm cutlass kernel assumes differently
         switch_w13 = getattr(self.quant_method, "load_up_proj_weight_first", False)
-        if (switch_w13 and shard_id == "w1") or (not switch_w13 and shard_id == "w3"):
+        if (
+            (switch_w13 and shard_id == "w1") or (not switch_w13 and shard_id == "w3")
+        ) and self.moe_runner_config.is_gated:
             start = shard_size
         else:
             start = 0
diff --git a/python/sglang/srt/layers/quantization/unquant.py b/python/sglang/srt/layers/quantization/unquant.py
index db69234cb..a1edcf5b4 100644
--- a/python/sglang/srt/layers/quantization/unquant.py
+++ b/python/sglang/srt/layers/quantization/unquant.py
@@ -58,6 +58,7 @@ if _is_npu:
 
 try:
     from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
+    from flashinfer.fused_moe.core import ActivationType
 except ImportError:
     flashinfer_cutlass_fused_moe = None
 
@@ -384,6 +385,11 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, MultiPlatformOp):
                 tp_size=layer.moe_tp_size,
                 tp_rank=layer.moe_tp_rank,
                 tune_max_num_tokens=next_power_of_2(x.shape[0]),
+                activation_type=(
+                    ActivationType.Relu2
+                    if moe_runner_config.activation == "relu2"
+                    else ActivationType.Swiglu
+                ),
             )[0]
             return StandardCombineInput(hidden_states=output)
         elif self.use_flashinfer_trtllm_moe:
diff --git a/python/sglang/srt/models/nemotron_h_mtp.py b/python/sglang/srt/models/nemotron_h_mtp.py
index 0d42cf168..dabd4b4ae 100644
--- a/python/sglang/srt/models/nemotron_h_mtp.py
+++ b/python/sglang/srt/models/nemotron_h_mtp.py
@@ -297,7 +297,7 @@ class NemotronHForCausalLMMTP(NemotronHForCausalLM):
         self.model = NemotronHMultiTokenPredictor(
             config=config,
             quant_config=quant_config,
-            prefix=add_prefix("model", prefix),
+            prefix=add_prefix("mtp", prefix),
         )
 
         self.lm_head = ParallelLMHead(
diff --git a/test/registered/model_loading/test_modelopt_loader.py b/test/registered/model_loading/test_modelopt_loader.py
index 606ddd2f0..97a6adf6d 100644
--- a/test/registered/model_loading/test_modelopt_loader.py
+++ b/test/registered/model_loading/test_modelopt_loader.py
@@ -561,5 +561,64 @@ class TestModelOptLoaderIntegration(CustomTestCase):
         self.assertEqual(server_args.model_path, "TinyLlama/TinyLlama-1.1B-Chat-v1.0")
 
 
+class TestParseQuantHfConfig(CustomTestCase):
+    """Tests for _parse_quant_hf_config and _parse_modelopt_quant_config.
+
+    Regression tests for the fix where quant_method='modelopt' ignoring quant_algo.
+    """
+
+    # (quant_config_input, expected_quant_method)
+    _MODELOPT_CASES = [
+        ({"quant_method": "modelopt", "quant_algo": "FP8"}, "modelopt_fp8"),
+        ({"quant_method": "modelopt", "quant_algo": "FP4"}, "modelopt_fp4"),
+        ({"quant_method": "modelopt", "quant_algo": "NVFP4"}, "modelopt_fp4"),
+        ({"quant_method": "modelopt", "quant_algo": "MIXED_PRECISION"}, "w4afp8"),
+        ({"quant_algo": "FP8"}, "modelopt_fp8"),
+        ({"quant_algo": "FP4"}, "modelopt_fp4"),
+        ({"quant_algo": "MIXED_PRECISION"}, "w4afp8"),
+        ({"quant_method": "modelopt"}, "modelopt"),
+    ]
+
+    def setUp(self):
+        """Set up a real ModelConfig using TinyLlama (already used elsewhere)."""
+        self.mock_tp_rank = patch(
+            "sglang.srt.distributed.parallel_state.get_tensor_model_parallel_rank",
+            return_value=0,
+        )
+        self.mock_tp_rank.start()
+
+        self.mock_mp_is_initialized = patch(
+            "sglang.srt.distributed.parallel_state.model_parallel_is_initialized",
+            return_value=True,
+        )
+        self.mock_mp_is_initialized.start()
+
+        self.model_config = ModelConfig(
+            model_path="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+        )
+
+    def tearDown(self):
+        self.mock_tp_rank.stop()
+        self.mock_mp_is_initialized.stop()
+
+    def test_modelopt_quant_parsing(self):
+        """Modelopt quant configs must resolve to the correct quant_method."""
+        for quant_cfg_input, expected in self._MODELOPT_CASES:
+            with self.subTest(quant_cfg=quant_cfg_input):
+                self.model_config.hf_config.quantization_config = dict(quant_cfg_input)
+                result = self.model_config._parse_quant_hf_config()
+                self.assertEqual(result["quant_method"], expected)
+
+    def test_non_modelopt_quant_method_unchanged(self):
+        """Non-modelopt quant_method (e.g. 'gptq') must NOT enter the modelopt path."""
+        self.model_config.hf_config.quantization_config = {
+            "quant_method": "gptq",
+            "bits": 4,
+        }
+        result = self.model_config._parse_quant_hf_config()
+        self.assertEqual(result["quant_method"], "gptq")
+        self.assertNotIn("quant_algo", result)
+
+
 if __name__ == "__main__":
     unittest.main()