docs: document video-capable multimodal models (#12565)

docs/supported_models/multimodal_language_models.md

@@ -46,6 +46,49 @@ in the GitHub search bar.
 | **DotsVLM-OCR**            | `rednote-hilab/dots.ocr`                   | Specialized OCR variant of DotsVLM optimized for optical character recognition tasks with enhanced text extraction and document understanding capabilities. | Don't use `--trust-remote-code` |
 | **NVILA** (8B, 15B, Lite-2B, Lite-8B, Lite-15B) | `Efficient-Large-Model/NVILA-8B` | `chatml` | NVILA explores the full stack efficiency of multi-modal design, achieving cheaper training, faster deployment and better performance. |
 
+## Video Input Support
+
+SGLang supports video input for Vision-Language Models (VLMs), enabling temporal reasoning tasks such as video question answering, captioning, and holistic scene understanding. Video clips are decoded, key frames are sampled, and the resulting tensors are batched together with the text prompt, allowing multimodal inference to integrate visual and linguistic context.
+
+| Model Family | Example Identifier | Video notes |
+|--------------|--------------------|-------------|
+| **Qwen-VL** (Qwen2-VL, Qwen2.5-VL, Qwen3-VL, Qwen3-Omni) | `Qwen/Qwen3-VL-235B-A22B-Instruct` | The processor gathers `video_data`, runs Qwen's frame sampler, and merges the resulting features with text tokens before inference. |
+| **GLM-4v** (4.5V, 4.1V, MOE) | `zai-org/GLM-4.5V` | Video clips are read with Decord, converted to tensors, and passed to the model alongside metadata for rotary-position handling. |
+| **NVILA** (Full & Lite) | `Efficient-Large-Model/NVILA-8B` | The runtime samples eight frames per clip and attaches them to the multimodal request when `video_data` is present. |
+| **LLaVA video variants** (LLaVA-NeXT-Video, LLaVA-OneVision) | `lmms-lab/LLaVA-NeXT-Video-7B` | The processor routes video prompts to the LlavaVid video-enabled architecture, and the provided example shows how to query it with `sgl.video(...)` clips. |
+
+Use `sgl.video(path, num_frames)` when building prompts to attach clips from your SGLang programs.
+
+Example OpenAI-compatible request that sends a video clip:
+
+```python
+import requests
+
+url = "http://localhost:30000/v1/chat/completions"
+
+data = {
+    "model": "Qwen/Qwen3-VL-30B-A3B-Instruct",
+    "messages": [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What’s happening in this video?"},
+                {
+                    "type": "video_url",
+                    "video_url": {
+                        "url": "https://github.com/sgl-project/sgl-test-files/raw/refs/heads/main/videos/jobs_presenting_ipod.mp4"
+                    },
+                },
+            ],
+        }
+    ],
+    "max_tokens": 300,
+}
+
+response = requests.post(url, json=data)
+print(response.text)
+```
+
 ## Usage Notes
 
 ### Performance Optimization