diff --git a/vllm/model_executor/models/transformers.py b/vllm/model_executor/models/transformers.py
index 4ec2b683fc33a..f3b7263ca3872 100644
--- a/vllm/model_executor/models/transformers.py
+++ b/vllm/model_executor/models/transformers.py
@@ -694,6 +694,17 @@ class TransformersForCausalLM(TransformersBase):
         return logits
 
 
+def flatten_and_concat(x: list[torch.Tensor]) -> torch.Tensor:
+    """Flatten until a list of tensors can be concatenated then do concat"""
+
+    def _can_concat(x: list[torch.Tensor]):
+        return len(set(map(lambda _x: _x.shape[1:], x))) == 1
+
+    if _can_concat(x):
+        return torch.concat(x)
+    return flatten_and_concat(flatten_bn(x))
+
+
 @MULTIMODAL_REGISTRY.register_processor(
     MultiModalProcessor,
     info=MultiModalProcessingInfo,
@@ -766,8 +777,7 @@ class TransformersForMultimodalLM(TransformersForCausalLM, SupportsMultiModal):
             if isinstance(pixel_values, torch.Tensor):
                 pixel_values = flatten_bn(pixel_values).to(self.dtype)
             elif is_list_of(pixel_values, torch.Tensor):
-                pixel_values = flatten_bn(flatten_bn(pixel_values),
-                                          concat=True).to(self.dtype)
+                pixel_values = flatten_and_concat(pixel_values).to(self.dtype)
             else:
                 raise ValueError(
                     f"Unsupported pixel_values type {type(pixel_values)}. "