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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, 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

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. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
• TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
• Diffusion models — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
• Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)

For more information, checkout the following:

• vllm-omni architecture design and recent roadmaps
• vllm-omni announcement blogpost