Hongsheng Liuandhsliu_ustc
7ab582ae25
Signed-off-by: hsliu_ustc <hsliu_ustc@noreply.gitcode.com> Co-authored-by: hsliu_ustc <hsliu_ustc@noreply.gitcode.com>
67 lines
3.2 KiB
Markdown
67 lines
3.2 KiB
Markdown
---
|
|
hide:
|
|
- navigation
|
|
- toc
|
|
---
|
|
|
|
# Welcome to vLLM-Omni
|
|
|
|
<p align="center">
|
|
<picture>
|
|
<source media="(prefers-color-scheme: dark)" src="./source/logos/vllm-omni-logo.png">
|
|
<img alt="vllm-omni" src="./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 style="text-align:center">
|
|
<script async defer src="https://buttons.github.io/buttons.js"></script>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm-omni" data-show-count="true" data-size="large" aria-label="Star">Star</a>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm-omni/subscription" data-show-count="true" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm-omni/fork" data-show-count="true" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
|
|
</p>
|
|
|
|
## 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>
|
|
<source media="(prefers-color-scheme: dark)" src="./source/architecture/omni-modality-model-architecture.png">
|
|
<img alt="vllm-omni-arch" src="./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
|
|
- OpenAI-compatible API server
|
|
- [Full-duplex realtime serving](serving/realtime_duplex_api.md) 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)
|
|
|
|
For more information, checkout the following:
|
|
|
|
- [vllm-omni architecture design and recent roadmaps](https://docs.google.com/presentation/d/1aPj0OGl_-ZVoib-Qne5dGDAlrRFB-PdHl6E-EE99g8E/edit?usp=sharing)
|
|
- [vllm-omni announcement blogpost](https://blog.vllm.ai/2025/11/30/vllm-omni.html)
|