multimodal_language_models.md

# Multimodal Language Models

These models accept multi-modal inputs (e.g., images and text) and generate text output. They augment language models with multimodal encoders.

## Example launch Command

```shell
python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \  # example HF/local path
  --host 0.0.0.0 \
  --port 30000 \
```

> See the [OpenAI APIs section](https://docs.sglang.ai/basic_usage/openai_api_vision.html) for how to send multimodal requests.

## Supported models

Below the supported models are summarized in a table.

If you are unsure if a specific architecture is implemented, you can search for it via GitHub. For example, to search for `Qwen2_5_VLForConditionalGeneration`, use the expression:

```
repo:sgl-project/sglang path:/^python\/sglang\/srt\/models\// Qwen2_5_VLForConditionalGeneration
```

in the GitHub search bar.


| Model Family (Variants)    | Example HuggingFace Identifier             | Description                                                                                                                                                                                                     | Notes |
|----------------------------|--------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------|
| **Qwen-VL** | `Qwen/Qwen3-VL-235B-A22B-Instruct`              | Alibaba's vision-language extension of Qwen; for example, Qwen2.5-VL (7B and larger variants) can analyze and converse about image content.                                                                     |  |
| **DeepSeek-VL2**           | `deepseek-ai/deepseek-vl2`                 | Vision-language variant of DeepSeek (with a dedicated image processor), enabling advanced multimodal reasoning on image and text inputs.                                                                        |  |
| **Janus-Pro** (1B, 7B)     | `deepseek-ai/Janus-Pro-7B`                 | DeepSeek's open-source multimodal model capable of both image understanding and generation. Janus-Pro employs a decoupled architecture for separate visual encoding paths, enhancing performance in both tasks. |  |
| **MiniCPM-V / MiniCPM-o**  | `openbmb/MiniCPM-V-2_6`                    | MiniCPM-V (2.6, ~8B) supports image inputs, and MiniCPM-o adds audio/video; these multimodal LLMs are optimized for end-side deployment on mobile/edge devices.                                                 |  |
| **Llama 3.2 Vision** (11B) | `meta-llama/Llama-3.2-11B-Vision-Instruct` | Vision-enabled variant of Llama 3 (11B) that accepts image inputs for visual question answering and other multimodal tasks.                                                                                     |  |
| **LLaVA** (v1.5 & v1.6)    | *e.g.* `liuhaotian/llava-v1.5-13b`         | Open vision-chat models that add an image encoder to LLaMA/Vicuna (e.g. LLaMA2 13B) for following multimodal instruction prompts.                                                                               |  |
| **LLaVA-NeXT** (8B, 72B)   | `lmms-lab/llava-next-72b`                  | Improved LLaVA models (with an 8B Llama3 version and a 72B version) offering enhanced visual instruction-following and accuracy on multimodal benchmarks.                                                       |  |
| **LLaVA-OneVision**        | `lmms-lab/llava-onevision-qwen2-7b-ov`     | Enhanced LLaVA variant integrating Qwen as the backbone; supports multiple images (and even video frames) as inputs via an OpenAI Vision API-compatible format.                                                 |  |
| **Gemma 3 (Multimodal)**   | `google/gemma-3-4b-it`                     | Gemma 3's larger models (4B, 12B, 27B) accept images (each image encoded as 256 tokens) alongside text in a combined 128K-token context.                                                                        |  |
| **Kimi-VL** (A3B)          | `moonshotai/Kimi-VL-A3B-Instruct`          | Kimi-VL is a multimodal model that can understand and generate text from images.                                                                                                                                |  |
| **Mistral-Small-3.1-24B**  | `mistralai/Mistral-Small-3.1-24B-Instruct-2503` | Mistral 3.1 is a multimodal model that can generate text from text or images input. It also supports tool calling and structured output. |  |
| **Phi-4-multimodal-instruct**  | `microsoft/Phi-4-multimodal-instruct` | Phi-4-multimodal-instruct is the multimodal variant of the Phi-4-mini model, enhanced with LoRA for improved multimodal capabilities. It supports text, vision and audio modalities in SGLang. |  |
| **MiMo-VL** (7B)           | `XiaomiMiMo/MiMo-VL-7B-RL`                 | Xiaomi's compact yet powerful vision-language model featuring a native resolution ViT encoder for fine-grained visual details, an MLP projector for cross-modal alignment, and the MiMo-7B language model optimized for complex reasoning tasks. |  |
| **GLM-4.5V** (106B) /  **GLM-4.1V**(9B)           | `zai-org/GLM-4.5V`                   | GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning                                                                                                                                                                                                      | Use `--chat-template glm-4v` |
| **DotsVLM** (General/OCR)  | `rednote-hilab/dots.vlm1.inst`             | RedNote's vision-language model built on a 1.2B vision encoder and DeepSeek V3 LLM, featuring NaViT vision encoder trained from scratch with dynamic resolution support and enhanced OCR capabilities through structured image data training. |  |
| **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` |

## Usage Notes

### Performance Optimization

For multimodal models, you can use the `--keep-mm-feature-on-device` flag to optimize for latency at the cost of increased GPU memory usage:

- **Default behavior**: Multimodal feature tensors are moved to CPU after processing to save GPU memory
- **With `--keep-mm-feature-on-device`**: Feature tensors remain on GPU, reducing device-to-host copy overhead and improving latency, but consuming more GPU memory

Use this flag when you have sufficient GPU memory and want to minimize latency for multimodal inference.