diff --git a/README.md b/README.md
index 5b87ae838885..c119ad42ac4b 100644
--- a/README.md
+++ b/README.md
@@ -58,7 +58,7 @@ vLLM is fast with:
 - Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html)
 - Continuous batching of incoming requests
 - Fast model execution with CUDA/HIP graph
-- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8.
+- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [AutoRound](https://arxiv.org/abs/2309.05516),INT4, INT8, and FP8.
 - Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
 - Speculative decoding
 - Chunked prefill
diff --git a/vllm/model_executor/layers/quantization/auto_round.py b/vllm/model_executor/layers/quantization/auto_round.py
index a5e63843cf62..2d9f5e52bd65 100644
--- a/vllm/model_executor/layers/quantization/auto_round.py
+++ b/vllm/model_executor/layers/quantization/auto_round.py
@@ -8,6 +8,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
@@ -74,7 +75,7 @@ def __repr__(self) -> str:
                 f"group_size={self.group_size}, sym={self.sym})")
 
     @classmethod
-    def get_name(cls):  ## use str will trigger preci issue
+    def get_name(cls) -> QuantizationMethods:
         return "auto-round"
 
     @classmethod
@@ -142,18 +143,18 @@ def apply_awq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
                      prefix, layer.__class__.__name__, weight_bits, group_size,
                      sym)
         if backend == "auto" or "marlin" in backend:
+            AWQ_TYPE_MAP = {
+                4: scalar_types.uint4,
+                8: scalar_types.uint8,
+            }
+            use_marlin = (weight_bits
+                          in AWQ_TYPE_MAP) and check_marlin_supported(
+                              AWQ_TYPE_MAP[weight_bits], group_size, not sym)
+
             if isinstance(layer, FusedMoE):
-                use_marlin = check_moe_marlin_supports_layer(layer, group_size)
-            else:
+                use_marlin = use_marlin and check_moe_marlin_supports_layer(
+                    layer, group_size)
 
-                AWQ_TYPE_MAP = {
-                    4: scalar_types.uint4,
-                    8: scalar_types.uint8,
-                }
-                use_marlin = ((weight_bits, sym) in AWQ_TYPE_MAP
-                              and check_marlin_supported(
-                                  AWQ_TYPE_MAP[(weight_bits)], group_size,
-                                  not sym))
         else:
             use_marlin = False
         if use_marlin:
@@ -180,10 +181,11 @@ def apply_awq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
             from vllm.model_executor.layers.quantization.moe_wna16 import (
                 MoeWNA16Config)
             config = {
-                "linear_quant_method": "awq",
-                "weight_bits": weight_bits,
+                "quant_method": "awq",
+                "bits": weight_bits,
                 "group_size": group_size,
                 "zero_point": not sym,
+                "lm_head": False,
             }
             return MoeWNA16Config.from_config(config).get_quant_method(
                 layer, prefix)
@@ -213,18 +215,18 @@ def apply_gptq_quant_layer(self,
                      prefix, layer.__class__.__name__, weight_bits, group_size,
                      sym)
         if backend == "auto" or "marlin" in backend:
+            GPTQ_TYPE_MAP = {
+                (4, True): scalar_types.uint4b8,
+                (8, True): scalar_types.uint8b128,
+            }
+            use_marlin = ((weight_bits, sym) in GPTQ_TYPE_MAP
+                          and check_marlin_supported(
+                              GPTQ_TYPE_MAP[(weight_bits, sym)],
+                              group_size,
+                              has_zp=not sym))
             if isinstance(layer, FusedMoE):
-                use_marlin = check_moe_marlin_supports_layer(layer, group_size)
-            else:
-                GPTQ_TYPE_MAP = {
-                    (4, True): scalar_types.uint4b8,
-                    (8, True): scalar_types.uint8b128,
-                }
-                use_marlin = ((weight_bits, sym) in GPTQ_TYPE_MAP
-                              and check_marlin_supported(
-                                  GPTQ_TYPE_MAP[(weight_bits, sym)],
-                                  group_size,
-                                  has_zp=not sym))
+                use_marlin = use_marlin and check_moe_marlin_supports_layer(
+                    layer, group_size)
         else:
             use_marlin = False
         if use_marlin:
@@ -251,11 +253,11 @@ def apply_gptq_quant_layer(self,
                 from vllm.model_executor.layers.quantization.moe_wna16 import (
                     MoeWNA16Config)
                 config = {
-                    "linear_quant_method": "gptq",
-                    "weight_bits": weight_bits,
+                    "quant_method": "gptq",
+                    "bits": weight_bits,
                     "group_size": group_size,
                     "sym": sym,
-                    "lm_head_quantized": False,
+                    "lm_head": False,
                 }
                 return MoeWNA16Config.from_config(config).get_quant_method(
                     layer, prefix)