hsliuustc0106
e1c4b6153a
Per review, the warmup recipe is a property of the model's request geometry, not of distributed layerwise offload: rename dlo_dummy_run_recipe to dummy_run_recipe and drop the DLO gate in _make_dummy_request. A model whose geometry needs recipe-specific sampling arguments warms up at startup whatever features are enabled — under DLO the generation also primes the component cache-retention budget; without it, the same warmup covers any other first-request cost. Validated both ways on L20X: DLO boot unchanged (releases stay in the startup window) and a non-DLO MiniMax-H3 boot now runs the warmup and serves normally. Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.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, 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
- Full-duplex realtime serving with streaming audio input and output (experimental)
vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
- Omni-modality models (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
- TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, 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: