diff --git a/vllm/model_executor/layers/quantization/gptq.py b/vllm/model_executor/layers/quantization/gptq.py
index f18c936bac60..2272709f9309 100644
--- a/vllm/model_executor/layers/quantization/gptq.py
+++ b/vllm/model_executor/layers/quantization/gptq.py
@@ -37,6 +37,7 @@ def __init__(
         desc_act: bool,
         lm_head_quantized: bool,
         dynamic: dict[str, dict[str, Union[int, bool]]],
+        autoround_version: str = "",
     ) -> None:
         # GPTQModel use `dynamic` config property to allow per module
         # quantization config so each module can be individually optimized.
@@ -74,6 +75,9 @@ def __init__(
                 "Currently, only 2/3/4/8-bit weight quantization is "
                 f"supported for GPTQ, but got {self.weight_bits} bits.")
 
+        # used to identify GPTQ model quantized by autoround
+        self.autoround_version = autoround_version
+
     def __repr__(self) -> str:
         return (f"GPTQConfig(weight_bits={self.weight_bits}, "
                 f"group_size={self.group_size}, "
@@ -108,8 +112,10 @@ def from_config(cls, config: dict[str, Any]) -> "GPTQConfig":
         desc_act = cls.get_from_keys(config, ["desc_act"])
         lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
                                                  default=False)
+        autoround_version = cls.get_from_keys_or(config, ["autoround_version"],
+                                                 default="")
         return cls(weight_bits, group_size, desc_act, lm_head_quantized,
-                   dynamic)
+                   dynamic, autoround_version)
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py
index 350975966668..3644d91f64e3 100644
--- a/vllm/model_executor/layers/quantization/gptq_marlin.py
+++ b/vllm/model_executor/layers/quantization/gptq_marlin.py
@@ -119,6 +119,9 @@ def __init__(self, weight_bits: int, group_size: int, desc_act: bool,
 
         self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)]
 
+        # used to identify GPTQ model quantized by autoround
+        self.autoround_version = full_config.get("autoround_version", "")
+
     def __repr__(self) -> str:
         return (f"GPTQMarlinConfig(quant_type={self.quant_type}, "
                 f"group_size={self.group_size}, "
diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py
index 94e6a66bea5c..a7e0a00350e6 100644
--- a/vllm/model_executor/models/qwen3_moe.py
+++ b/vllm/model_executor/models/qwen3_moe.py
@@ -159,9 +159,13 @@ def __init__(
 
     def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
         # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
-        # seems to avoid gate quantization.
-        # See: https://huggingface.co/Qwen/Qwen3-30B-A3B-GPTQ-Int4
-        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+        # seems to avoid gate quantization while AutoRound does.
+        # See: https://huggingface.co/Qwen/Qwen3-30B-A3B-GPTQ-Int4,
+        # and https://huggingface.co/jart25/Qwen3-Coder-30B-A3B-Instruct-Int4-gptq
+        if isinstance(
+                quant_config,
+            (GPTQConfig,
+             GPTQMarlinConfig)) and not quant_config.autoround_version:
             return None
         return quant_config