Easy, fast, and cheap omni-modality model serving for everyone

| Documentation | Weekly Meeting |

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Latest News 🔥

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

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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

Install vLLM with pip or from source:

pip install vllm

🛠️ Installation

Environment setup

Use Docker to keep consistent basic environment (Optional, Recommended)

docker run --gpus all --ipc=host --network=host -v $source_dir:$container_dir --name $container_name -it nvcr.io/nvidia/pytorch:25.01-py3 bash

Set up basic uv environment

pip install uv
uv venv --python 3.12 --seed
source .venv/bin/activate

Installation of vLLM (for users)

Now we build it based on vLLM v0.11.0. Please install it with command below.

uv pip install vllm==0.11.0 --torch-backend=auto

Installation of vLLM (for developers)

If you want to check or debug with source code of vLLM, install stable release version of vllm with 0.11.0 from source with pre-built wheel file.

git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout v0.11.0

Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set VLLM_PRECOMPILED_WHEEL_LOCATION as your local absolute path of whl file.

export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.11.0/vllm-0.11.0-cp38-abi3-manylinux1_x86_64.whl

Install vllm with command below (If you have no existing PyTorch).

uv pip install --editable .

Install vllm with command below (If you already have PyTorch).

python use_existing_torch.py
uv pip install -r requirements/build.txt
uv pip install --no-build-isolation --editable .

Installation of vLLM-omni

Install additional requirements for vllm-omni

git clone https://github.com/vllm-project/vllm-omni.git
cd vllm_omni
uv pip install -e .

Run examples (Qwen2.5-omni)

Please check the folder of examples

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.