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Signed-off-by: Taichang Zhou <tzhouam@connect.ust.hk> Signed-off-by: tzhouam <tzhouam@connect.ust.hk> Signed-off-by: princepride <wangzhipeng628@gmail.com> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Isotr0py <Isotr0py@outlook.com> Co-authored-by: ZJY0516 <zhu.jiangyun@foxmail.com> Co-authored-by: youkaichao <youkaichao@gmail.com>
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Welcome to vLLM-Omni
Easy, fast, and cheap omni-modality model serving for everyone
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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, 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
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. Qwen2.5-Omni, Qwen3-Omni)
- Multi-modality generation models (e.g. Qwen-Image)
For more information, checkout the following: