diff --git a/tests/models/encoder_decoder/vision_language/test_mllama.py b/tests/models/encoder_decoder/vision_language/test_mllama.py
index 260d2c109387..c688655887e2 100644
--- a/tests/models/encoder_decoder/vision_language/test_mllama.py
+++ b/tests/models/encoder_decoder/vision_language/test_mllama.py
@@ -212,7 +212,7 @@ def _run_test(
     with vllm_runner(model,
                      dtype=dtype,
                      max_model_len=4096,
-                     max_num_seqs=2,
+                     max_num_seqs=3,
                      tensor_parallel_size=tensor_parallel_size,
                      distributed_executor_backend=distributed_executor_backend,
                      limit_mm_per_prompt={"image": _LIMIT_IMAGE_PER_PROMPT
diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py
index 68d5298dfc9b..6a2e20840fcf 100644
--- a/vllm/model_executor/models/mllama.py
+++ b/vllm/model_executor/models/mllama.py
@@ -1235,11 +1235,34 @@ def sample(
         next_tokens = self.sampler(logits, sampling_metadata)
         return next_tokens
 
+    def unpack_data(self,
+                    image_data: Union[List[torch.Tensor], torch.Tensor],
+                    padding_value=0) -> torch.Tensor:
+        if isinstance(image_data, torch.Tensor):
+            # torch.Tensor
+            return image_data
+        else:
+            assert isinstance(
+                image_data[0],
+                torch.Tensor), "Image data is not properly batched."
+            # List[torch.Tensor]
+            bsz = len(image_data)
+            max_length = max(t.size(0) for t in image_data)
+            trailing_dims = image_data[0].shape[1:]
+            for data in image_data:
+                cur_trailing_dims = data.shape[1:]
+                assert cur_trailing_dims == trailing_dims
+            output_tensor = torch.full((bsz, max_length, *trailing_dims),
+                                       padding_value,
+                                       dtype=image_data[0].dtype,
+                                       device=image_data[0].device)
+            for i, t in enumerate(image_data):
+                output_tensor[i, :t.size(0)] = t
+            return output_tensor
+
     def _parse_and_validate_image_input(self, **kwargs: object):
         # tensor with the same shape will be batched together by
         # MultiModalKwargs.batch, so pixel_values here can be:
-        #   - List[List[torch.Tensor]]:
-        #       with shape (num_tiles, 3, image_res, image_res)
         #   - List[torch.Tensor]:
         #       with shape (num_image, num_tiles, 3, image_res, image_res)
         #   - torch.Tensor:
@@ -1274,10 +1297,9 @@ def _parse_and_validate_image_input(self, **kwargs: object):
 
             return MllamaImagePixelInputs(
                 type="pixel_values",
-                data=pixel_values,
-                aspect_ratio_ids=aspect_ratio_ids,
-                aspect_ratio_mask=aspect_ratio_mask,
-            )
+                data=self.unpack_data(pixel_values),
+                aspect_ratio_ids=self.unpack_data(aspect_ratio_ids),
+                aspect_ratio_mask=self.unpack_data(aspect_ratio_mask))
 
         if image_embeds is not None:
             raise NotImplementedError