vllm-project--vllm-omni/docs/getting_started/installation/gpu/rocm.inc.md

--8<-- [start:requirements]

• GPU: Validated on gfx942 (It should be supported on the AMD GPUs that are supported by vLLM.)

--8<-- [end:requirements]

--8<-- [start:set-up-using-python]

vLLM-Omni current recommends the steps in under setup through Docker Images.

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

Installation of vLLM

vLLM-Omni is built based on vLLM. Please install it with command below.

uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723

Installation of vLLM-Omni

# we need to add --no-build-isolation as the torch
# is not obtained from pypi, we have to install using the
# torch installed in our environment
uv pip install vllm-omni

# Optional if want to run Qwen3 TTS
uv pip uninstall onnxruntime # should be removed before we can install onnxruntime-rocm
uv pip install onnxruntime-rocm

--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

uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723

The pre-built 0.25.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack 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

git clone https://github.com/vllm-project/vllm-omni.git
cd vllm-omni
VLLM_OMNI_TARGET_DEVICE=rocm uv pip install -e .
# OR
uv pip install -e . --no-build-isolation

(Optional) Installation of vLLM from source

If you want to check, modify or debug with source code of vLLM, install the library from source with the following instructions:

git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout v0.25.0
python3 -m pip install -r requirements/rocm.txt
python3 setup.py develop

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

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

Build docker image

DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-omni-rocm .

Launch the docker image

Launch with OpenAI API Server

docker run --rm \
--group-add=video \
--ipc=host \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
--device /dev/kfd \
--device /dev/dri \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=$HF_TOKEN" \
-p 8091:8091 \
--ipc=host \
vllm-omni-rocm \
--model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 8091

Launch with interactive session for development

docker run --rm -it \
--network=host \
--group-add=video \
--ipc=host \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
--device /dev/kfd \
--device /dev/dri \
-v <path/to/model>:/app/model \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--entrypoint bash \
vllm-omni-rocm

--8<-- [end:build-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-rocm. The version of vLLM-Omni indicates which release of vLLM it is based on.

Launch vLLM-Omni Server

Here's an example deployment command that has been verified on 2 x MI300's:

docker run --rm \
  --group-add=video \
  --ipc=host \
  --cap-add=SYS_PTRACE \
  --security-opt seccomp=unconfined \
  --device /dev/kfd \
  --device /dev/dri \
  -v <path/to/model>:/app/model \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --env "HF_TOKEN=$HF_TOKEN" \
  -p 8091:8091 \
  vllm/vllm-omni-rocm:v0.25.0 \
  --model Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091

Launch an interactive terminal with prebuilt docker image.

If you want to run in dev environment you can launch the docker image as follows:

docker run --rm -it \
  --network=host \
  --group-add=video \
  --ipc=host \
  --cap-add=SYS_PTRACE \
  --security-opt seccomp=unconfined \
  --device /dev/kfd \
  --device /dev/dri \
  -v <path/to/model>:/app/model \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --env "HF_TOKEN=$HF_TOKEN" \
  --entrypoint bash \
  vllm/vllm-omni-rocm:v0.25.0

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