diff --git a/docs/source/models/vlm.rst b/docs/source/models/vlm.rst
index 8f5aa58f9f2b9..45316fd34a5d2 100644
--- a/docs/source/models/vlm.rst
+++ b/docs/source/models/vlm.rst
@@ -135,6 +135,33 @@ Instead of passing in a single image, you can pass in a list of images.
 
 A code example can be found in `examples/offline_inference_vision_language_multi_image.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_vision_language_multi_image.py>`_.
 
+Multi-image input can be extended to perform video captioning. We show this with `Qwen2-VL <https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct>`_ as it supports videos: 
+
+.. code-block:: python
+
+    # Specify the maximum number of frames per video to be 4. This can be changed. 
+    llm = LLM("Qwen/Qwen2-VL-2B-Instruct", limit_mm_per_prompt={"image": 4})
+
+    # Create the request payload.
+    video_frames = ... # load your video making sure it only has the number of frames specified earlier.
+    message = {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Describe this set of frames. Consider the frames to be a part of the same video."},
+        ],
+    }
+    for i in range(len(video_frames)):
+        base64_image = encode_image(video_frames[i]) # base64 encoding.
+        new_image = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
+        message["content"].append(new_image)
+
+    # Perform inference and log output.
+    outputs = llm.chat([message])
+    
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+
 Online Inference
 ----------------