diff --git a/fastdeploy/worker/xpu_model_runner.py b/fastdeploy/worker/xpu_model_runner.py
index 8be2e785fd9..f6864570874 100644
--- a/fastdeploy/worker/xpu_model_runner.py
+++ b/fastdeploy/worker/xpu_model_runner.py
@@ -110,7 +110,8 @@ def __init__(
 
         # VL model config:
         if self.enable_mm:
-            self._init_image_preprocess()
+            if "ernie" in self.fd_config.model_config.model_type:
+                self._init_image_preprocess()
 
             self.amp_black = [
                 "reduce_sum",
@@ -1001,7 +1002,7 @@ def _init_share_inputs(self, max_num_seqs: int):
 
         if self.enable_mm:
             head_dim = self.model_config.head_dim
-            if "paddleocr" in self.model_config.model_type:  # neox style = True
+            if "qwen" in self.model_config.model_type or "paddleocr" in self.model_config.model_type:  # neox style = True
                 rope_head_dim = head_dim
             else:  # neox style = False
                 rope_head_dim = head_dim // 2
@@ -1697,7 +1698,7 @@ def _preprocess_mm_task(self, one: dict) -> None:
             image_type_ids = one["image_type_ids"][np.newaxis, :]
             images = one["images"]
             image_type_ids = paddle.to_tensor(image_type_ids, dtype=paddle.int64)
-            images = paddle.to_tensor(images, dtype="uint8")
+            images = paddle.to_tensor(images, dtype="uint8" if "ernie" in self.model_config.model_type else "bfloat16")
             grid_thw = paddle.to_tensor(one["grid_thw"], dtype="int64")
         else:
             image_type_ids = None
@@ -1752,6 +1753,22 @@ def extract_vision_features_ernie(self, vision_inputs: dict[str, list[paddle.Ten
             )
         return image_features
 
+    def extract_vision_features_qwen(self, vision_inputs: dict[str, list[paddle.Tensor]]) -> paddle.Tensor:
+        assert len(vision_inputs["images_lst"]) > 0, "at least one image needed"
+
+        grid_thw = paddle.to_tensor(vision_inputs["grid_thw_lst"], dtype=paddle.int64)
+        images = paddle.concat(vision_inputs["images_lst"]).cast("bfloat16")
+        with paddle.amp.auto_cast(
+            True,
+            custom_black_list=self.amp_black,
+            custom_white_list=self.amp_white,
+            level="O2",
+            dtype=self.model_config.dtype,
+        ):
+            image_features = self.model.visual.extract_feature(images, grid_thw)
+
+        return image_features
+
     def extract_vision_features_paddleocr(self, inputs: dict[str, list[paddle.Tensor]]) -> paddle.Tensor:
         if envs.FD_ENABLE_MAX_PREFILL:
             inputs["vit_position_ids_lst"] = np.concatenate(inputs["vit_position_ids_lst"])
@@ -1800,9 +1817,8 @@ def extract_vision_features(self, multi_vision_inputs: dict[str, list[paddle.Ten
         """extract_vision_features"""
         if "ernie" in self.model_config.model_type:
             return self.extract_vision_features_ernie(multi_vision_inputs)
-        # TODO support VL
-        # elif "qwen" in self.model_config.model_type:
-        #     return self.extract_vision_features_qwen(multi_vision_inputs)
+        elif "qwen" in self.model_config.model_type:
+            return self.extract_vision_features_qwen(multi_vision_inputs)
         elif "paddleocr" in self.model_config.model_type:
             return self.extract_vision_features_paddleocr(multi_vision_inputs)
         else: