vllm-omni 0.31.0rc1

Easy, fast, and cheap omni-modality model serving for everyone

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Latest News 🔥

• [2026/09] We released 0.30.0, rebased onto vLLM 0.30.0, featuring a unified full-duplex serving framework around engine-owned sessions for MiniCPM-o 4.5 and AURA, native cross-stage KV and multimodal payload transfer (Mooncake AR-to-DiT handoff and NIXL connectors), interactive world-model serving with LingBot World, and realtime MiniMax H3 Turbo inference on NVIDIA Blackwell and Ascend 950.
• [2026/08] We released 0.28.0, featuring production-ready MiniMax H3 serving on GPU and NPU, a unified AR/DiT paged KV cache runtime, and enhanced realtime full-duplex serving for the MiniCPM-o series.
• [2026/08] VeRL-Omni v0.2.0 is released: faster diffusion RL powered by vLLM-Omni (request-level/step-wise batching with FA3), rebuilt Qwen3-Omni multimodal training (DPO & GSPO), plus LTX-2.3, Qwen-Image-Edit support and more. See the release notes.
• [2026/08] We released 0.26.0 - aligned with the vLLM 0.26 release line, featuring MiniMax H3 joint video/audio generation, an experimental full-duplex realtime runtime for MiniCPM-o 4.5, distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support.
• [2026/07] We released 0.24.0 - 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] Starting with 0.14.0, vLLM-Omni publishes a stable release aligned with every even-numbered upstream vLLM minor version. 0.16.0, 0.18.0, 0.20.0, and 0.22.0 continued this cadence, expanding omni and world-model support with NVIDIA Cosmos3 and DreamZero, adding models such as MiniCPM-o 4.5, MOSS-TTS, and Lance, and advancing TTS, diffusion, distributed execution, quantization, RL integration through VeRL-Omni, and CUDA/ROCm/MUSA/NPU/XPU coverage.
• [2026/03] Check out our first public project deepdive at the vLLM Hong Kong Meetup!
• [2025/11] vLLM community officially released vllm-project/vllm-omni in order to support omni-modality models serving.

About

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

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
• OpenAI-compatible API server
• Full-duplex realtime serving with streaming audio input and output

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, Tencent AuK, Breeze-TTS-2, CosyVoice3)
• Diffusion models — image, video, and audio generation (e.g. MiniMax H3, LingBot World, MAGI-2, LTX-2.5, Wan2.2)
• Robot-policy and action models (e.g. π0.5, GR00T-N1.7, DreamZero-DROID, InternVLA-A1)

Getting Started

Visit our documentation to learn more.

• Installation
• Quickstart
• Nightly CUDA Docker images built from the latest main
• List of Supported Models
• Deployment Recipes for vLLM-Omni model serving

Contributing

We welcome and value any contributions and collaborations. Please check out Contributing to vLLM-Omni for how to get involved.

Citation

If you use vLLM-Omni for your research, please cite our paper:

@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 or vLLM user forum at discuss.vllm.ai.

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License

Apache License 2.0, as found in the LICENSE file.