tzhouamandClaude Opus 5
7869a8dc0d
Single-stream CPU decode reads every weight to emit one token, so bits per weight set the token rate. This registers a W4A16 group-wise method out-of-tree through register_quantization_config -- vLLM itself is untouched -- using PyTorch's _weight_int4pack_mm_for_cpu tinygemm kernel. Two paths over one set of weights, because that kernel dequantizes once per row of activations and so is linear in the batch (12.9x slower than bf16 at 512 rows). Single-row steps take tinygemm; wider batches unpack the weight to bf16 once and use the ordinary oneDNN/AMX GEMM. Shipping only the first measured pp512 = 557 tok/s against bf16's 4455; with both, 3589. The batch threshold is 2 rather than the ~8 rows where the unpack path starts winning on speed, because running tinygemm on a multi-row batch and then vLLM's cpu_attention_with_kv_cache in the same forward dies with SIGILL inside the attention kernel -- an AMX instruction with no valid tile config loaded. The same prompt is fine in bf16 and fine once every multi-row GEMM takes the unpack path. Positioning, measured on a Xeon 8480C with 32 pinned cores and a warm cache (analysis/spark_cpu_int4.md): vLLM's own GPTQ W4A8 AMX path is faster -- 88.7 tok/s decode and 4355 prefill against 85.5 and 3589 here -- but its config rejects sym=False at 4 bits, so it can only be fed a symmetric grid. This one is asymmetric and costs +12.5% perplexity against that path's +27.2%, and needs no AMX. llama.cpp Q4_K_M, for reference, is 98.0 tok/s decode and 1356 prefill at +7.6% and 5.15 bits per weight. Expanding a quantized checkpoint back to bf16 offline and scoring it reproduces the quantized run to within 0.2% perplexity, so both GEMM paths, the nibble packing and the quantized tied embedding are faithful. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01GXhbAfkSNRaeqZrus5Z2fx
Easy, fast, and cheap omni-modality model serving for everyone
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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.
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License
Apache License 2.0, as found in the LICENSE file.