# --8<-- [start:requirements]

- GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)

# --8<-- [end:requirements]
# --8<-- [start:set-up-using-python]

vLLM-Omni depends vLLM. So please follow instructions below mainly for vLLM.

!!! note
    PyTorch installed via `conda` will statically link `NCCL` library, which can cause issues when vLLM tries to use `NCCL`. See <gh-issue:8420> for more details.

In order to be performant, vLLM has to compile many cuda kernels. The compilation unfortunately introduces binary incompatibility with other CUDA versions and PyTorch versions, even for the same PyTorch version with different building configurations.

Therefore, it is recommended to install vLLM and vLLM-Omni with a **fresh new** environment. If either you have a different CUDA version or you want to use an existing PyTorch installation, you need to build vLLM from source. See [build-from-source-vllm](https://docs.vllm.ai/en/stable/getting_started/installation/gpu/#build-wheel-from-source) for more details.

# --8<-- [start:pre-built-wheels]

#### Installation of vLLM

vLLM-Omni is built based on vLLM. Please install it with command below.
```bash
uv pip install vllm==0.25.0 --torch-backend=auto
```

#### Installation of vLLM-Omni

```bash
uv pip install vllm-omni
```

To run Gradio demos, also install the optional extras:
```bash
uv pip install 'vllm-omni[demo]'
```

# --8<-- [end:pre-built-wheels]

# --8<-- [start:build-wheel-from-source]

#### Installation of vLLM
If you do not need to modify source code of vLLM, you can directly install the stable 0.25.0 release version of the library

```bash
uv pip install vllm==0.25.0 --torch-backend=auto
```

The 0.25.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.

#### Installation of vLLM-Omni
Since vllm-omni is rapidly evolving, it's recommended to install it from source
```bash
git clone https://github.com/vllm-project/vllm-omni.git
cd vllm-omni
uv pip install -e .
```

To run Gradio demos, install with optional extras:
```bash
uv pip install -e '.[demo]'
```

<details><summary>(Optional) Installation of vLLM from source</summary>
If you want to check, modify or debug with source code of vLLM, install the library from source with the following instructions:

```bash
git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout v0.25.0
```
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.
```bash
#For CUDA 13.0 (the default for v0.25.0; the wheel filename has no `+cu130` suffix)
export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.25.0/vllm-0.25.0-cp38-abi3-manylinux_2_28_x86_64.whl
```
Install vllm with command below (If you have no existing PyTorch).
```bash
uv pip install --editable .
```
Install vllm with command below (If you already have PyTorch).
```bash
python use_existing_torch.py
uv pip install -r requirements/build/cuda.txt
uv pip install --no-build-isolation --editable .
```
</details>

# --8<-- [end:build-wheel-from-source]

# --8<-- [start:build-wheel-from-source-in-docker]

# --8<-- [end:build-wheel-from-source-in-docker]

# --8<-- [start:pre-built-images]

vLLM-Omni offers an official docker image for deployment. These images are built on top of vLLM docker images and available on Docker Hub as [vllm/vllm-omni](https://hub.docker.com/r/vllm/vllm-omni/tags). The version of vLLM-Omni indicates which release of vLLM it is based on.

Here's an example deployment command that has been verified on 2 x H100's:
```bash
docker run --runtime nvidia --gpus 2 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=$HF_TOKEN" \
    -p 8091:8091 \
    --ipc=host \
    vllm/vllm-omni:v0.25.0 \
    vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
```

!!! tip
    The CUDA image does not define a default entrypoint, so include `vllm serve ... --omni` after the image name.

# --8<-- [end:pre-built-images]

# --8<-- [start:build-docker]

#### Build docker image

```bash
DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.cuda -t vllm-omni-cuda .
```

If you want to specify the base vLLM version:

```bash
DOCKER_BUILDKIT=1 docker build \
  -f docker/Dockerfile.cuda \
  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.22.1 \
  -t vllm-omni-cuda .
```

#### Launch the docker image

##### Launch with OpenAI API Server

!!! note
    The model `Qwen/Qwen3-Omni-30B-A3B-Instruct` requires significant GPU memory. The example below has been verified on 2 x H100's.

```bash
docker run --runtime nvidia --gpus 2 \
  -v ${HF_HOME:-$HOME/.cache/huggingface}:/root/.cache/huggingface \
  --env "HF_TOKEN=$HF_TOKEN" \
  -p 8091:8091 \
  --ipc=host \
  vllm-omni-cuda \
  vllm serve --omni --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 8091
```

By default, this mounts `$HOME/.cache/huggingface` as the model cache directory. To use a custom location, set the `HF_HOME` environment variable before running the command (e.g., `export HF_HOME=/data/models`).

##### Launch with interactive session for development

```bash
docker run --runtime nvidia --gpus all -it --rm \
  -v ${HF_HOME:-$HOME/.cache/huggingface}:/root/.cache/huggingface \
  --env "HF_TOKEN=$HF_TOKEN" \
  -p 8091:8091 \
  --ipc=host \
  --entrypoint bash \
  vllm-omni-cuda
```

# --8<-- [end:build-docker]