103 lines
7.3 KiB
Markdown
103 lines
7.3 KiB
Markdown
<p align="center">
|
|
<picture>
|
|
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/logos/vllm-omni-logo.png">
|
|
<img alt="vllm-omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/logos/vllm-omni-logo.png" width=55%>
|
|
</picture>
|
|
</p>
|
|
<h3 align="center">
|
|
Easy, fast, and cheap omni-modality model serving for everyone
|
|
</h3>
|
|
|
|
<p align="center">
|
|
| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://deepwiki.com/vllm-project/vllm-omni"><b>DeepWiki</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> | <a href="docs/assets/WeChat.jpg"><b>WeChat</b></a> | <a href="https://arxiv.org/abs/2602.02204"><b>Paper</b></a> | <a href="https://docs.google.com/presentation/d/111-L8zF7A1j_YI_cR8JsblofdScdRr2f/edit?usp=sharing&ouid=110473603432222024453&rtpof=true&sd=true"><b>Slides</b></a> |
|
|
</p>
|
|
|
|
|
|
---
|
|
|
|
*Latest News* 🔥
|
|
- [2026/08] We released [0.26.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.26.0) - aligned with the vLLM 0.26 release line, featuring [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) joint video/audio generation, an experimental full-duplex realtime runtime for [MiniCPM-o 4.5](recipes/OpenBMB/MiniCPM-o-4_5.md), distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support.
|
|
- [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
|
|
- [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni).
|
|
- [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends.
|
|
- [2026/03] Starting with [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0), vLLM-Omni publishes a stable release for every even-numbered upstream vLLM minor version, aligned with the corresponding vLLM release. [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0) and [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) continued this cadence with broader omni, TTS, diffusion, distributed execution, performance, hardware, and production-readiness improvements.
|
|
- [2026/03] Check out our first public [project deepdive](https://youtu.be/sgwNfsNnR9I) at the vLLM Hong Kong Meetup!
|
|
- [2026/03] **[vllm-omni-skills](https://github.com/hsliuustc0106/vllm-omni-skills)** is a community-driven collection of AI assistant skills that help developers work with vLLM-Omni more effectively. These skills can be used with popular agentic AI coding assistants like **Cursor IDE**, **Claude**, **Codex**, and more.
|
|
- [2025/11] vLLM community officially released [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni) in order to support omni-modality models serving.
|
|
|
|
---
|
|
|
|
## About
|
|
|
|
[vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
|
|
|
|
- **Omni-modality**: Text, image, audio, video, and action data processing
|
|
- **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
|
|
- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs
|
|
|
|
<p align="center">
|
|
<picture>
|
|
<img alt="vllm-omni" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/omni-modality-model-architecture.png" width=55%>
|
|
</picture>
|
|
</p>
|
|
|
|
vLLM-Omni is fast with:
|
|
|
|
- State-of-the-art AR support by leveraging efficient KV cache management from vLLM
|
|
- Pipelined stage execution overlapping for high throughput performance
|
|
- Fully disaggregation based on OmniConnector and dynamic resource allocation across stages
|
|
|
|
vLLM-Omni is flexible and easy to use with:
|
|
|
|
- Heterogeneous pipeline abstraction to manage complex model workflows
|
|
- Seamless integration with popular Hugging Face models
|
|
- Tensor, pipeline, data and expert parallelism support for distributed inference
|
|
- Streaming outputs
|
|
- **Experimental:** Full-duplex realtime serving with streaming audio input and output for MiniCPM-o 4.5
|
|
- OpenAI-compatible API server
|
|
|
|
vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
|
|
|
|
- **Omni-modality models** (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
|
|
- **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
|
|
- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
|
|
- **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
|
|
|
|
## Getting Started
|
|
|
|
Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn more.
|
|
|
|
- [Installation](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/)
|
|
- [Quickstart](https://vllm-omni.readthedocs.io/en/latest/getting_started/quickstart/)
|
|
- [List of Supported Models](https://vllm-omni.readthedocs.io/en/latest/models/supported_models/)
|
|
- [Deployment Recipes](https://recipes.vllm.ai) for vLLM-Omni model serving
|
|
|
|
## Contributing
|
|
|
|
We welcome and value any contributions and collaborations.
|
|
Please check out [Contributing to vLLM-Omni](https://vllm-omni.readthedocs.io/en/latest/contributing/) for how to get involved.
|
|
|
|
## Citation
|
|
|
|
If you use vLLM-Omni for your research, please cite our [paper](https://arxiv.org/abs/2602.02204):
|
|
|
|
```bibtex
|
|
@article{yin2026vllmomni,
|
|
title={vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models},
|
|
author={Peiqi Yin, Jiangyun Zhu, Han Gao, Chenguang Zheng, Yongxiang Huang, Taichang Zhou, Ruirui Yang, Weizhi Liu, Weiqing Chen, Canlin Guo, Didan Deng, Zifeng Mo, Cong Wang, James Cheng, Roger Wang, Hongsheng Liu},
|
|
journal={arXiv preprint arXiv:2602.02204},
|
|
year={2026}
|
|
}
|
|
```
|
|
|
|
## Join the Community
|
|
Feel free to ask questions, provide feedbacks and discuss with fellow users of vLLM-Omni in `#sig-omni` slack channel at [slack.vllm.ai](https://slack.vllm.ai) or vLLM user forum at [discuss.vllm.ai](https://discuss.vllm.ai).
|
|
|
|
## Star History
|
|
|
|
[](https://www.star-history.com/#vllm-project/vllm-omni&type=date&legend=top-left)
|
|
|
|
## License
|
|
|
|
Apache License 2.0, as found in the [LICENSE](./LICENSE) file.
|