tzhouamandClaude Opus 5
6bad5a7cd4
The docstring said one layout costs 1.9x prefill. That came from the single surviving pass of a grid whose other passes had crashed on the AOT cache collision -- a number the harness itself had labelled "single pass, not a result", which I then published. Re-measured properly: caches isolated per arm so all passes complete, on idle node-1 cores with memory bound to the same node, pooled over 7 interleaved passes per arm. pp512 goes 2190 -> 1344 tok/s, i.e. 1.63x, not 1.9x. The ratio is only good to about 1.4-1.8x (worst pairing 1.84x, best 1.36x) because both dequantizing arms swing 10-18% here, while the tinygemm-only arm repeats to 0.3%. The existence and rough size of the cost is solid -- a 63% gap is far outside that band -- but the third digit is not, and the docstring now says so rather than implying precision it does not have. The comparison that is reliable is against prefill_dequant: false, whose arm is the steady one: 2.97x its prefill at the same memory, without routing multi-row batches into the tinygemm/AMX SIGILL hazard. That setting stays dominated. Also corrected a hypothesis of my own. The first three direct passes read 1344 -> 1421 -> 1555 and I called it a warm-up trend; four more passes gave 1271, 1335, 1443, 1309, so it was noise and three ascending points out of three at 12% spread meant nothing. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Easy, fast, and cheap omni-modality model serving for everyone
| Documentation | DeepWiki | User Forum | Developer Slack | WeChat | Paper | Slides |
Latest News 🔥
- [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
v0.2.0is 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, IndexTTS 2.5, dots.tts, CosyVoice3)
- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, LTX-2.5, SANA-Video, Wan2.2)
- Robot-policy and action models (e.g. π0, 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.
Star History
License
Apache License 2.0, as found in the LICENSE file.