Easy, fast, and cheap omni-modality model serving for everyone
| Documentation | Weekly Meeting |
Latest News 🔥
- [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 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:
- Seamless AR support for efficient KV Cache management with vLLM
- Pipelined stage execution overlapping
- Fully disaggregation based on omniConnector and dynamic resource allocation across stages
vLLM-omni is flexible and easy to use with:
- Seamless integration with popular Hugging Face models
- High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
- Tensor, pipeline, data and expert parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
- Multi-modality generation models (e.g. Qwen-image)
- Omni-modality models (e.g. Qwen-omni)
Getting Started
Visit our documentation to learn more.
Visit our documentation to learn more.
Contributing
We welcome and value any contributions and collaborations. Please check out Contributing to vLLM-omni for how to get involved.
Weekly Meeting
- vLLM Omni Weekly Meeting: https://tinyurl.com/vllm-omni-meeting
- Wednesday, 11:30 - 12:30 (UTC+8, Convert to your timezone)
License
Apache License 2.0, as found in the LICENSE file.