# vllm-omni

**Repository Path**: cdpro/vllm-omni

## Basic Information

- **Project Name**: vllm-omni
- **Description**: No description available
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-29
- **Last Updated**: 2025-12-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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Easy, fast, and cheap omni-modality model serving for everyone
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<p align="center">
| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
</p>

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*Latest News* 🔥

- [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.

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## 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, video, and audio 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 outputs

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    <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%>
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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

vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:

- Omni-modality models (e.g. Qwen-Omni)
- Multi-modality generation models (e.g. Qwen-Image)

## 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/)

## 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.

## 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).

## License

Apache License 2.0, as found in the [LICENSE](./LICENSE) file.