https://github.com/vllm-project/vllm-omni

A framework for efficient model inference with omni-modality models

| Documentation | User Forum | Developer Slack |

---

Latest News 🔥

• [2025/11] vLLM community officially released vllm-project/vllm-omni in order to support omni-modality models serving.

---

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

Visit our documentation to learn more.

• Installation
• Quickstart
• List of Supported Models

We welcome and value any contributions and collaborations. Please check out Contributing to vLLM-Omni for how to get involved.

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.

Apache License 2.0, as found in the LICENSE file.