llama-stack-mirror/docs/source/distributions/self_hosted_distro/remote-vllm.md

---

orphan: true

Remote vLLM Distribution

:maxdepth: 2
:hidden:

self

The llamastack/distribution-remote-vllm distribution consists of the following provider configurations:

API	Provider(s)
agents	inline::meta-reference
datasetio	remote::huggingface, inline::localfs
eval	inline::meta-reference
inference	remote::vllm
safety	inline::llama-guard
scoring	inline::basic, inline::llm-as-judge, inline::braintrust
telemetry	inline::meta-reference
tool_runtime	remote::brave-search, remote::tavily-search, inline::code-interpreter, inline::rag-runtime, remote::model-context-protocol
vector_io	inline::faiss, remote::chromadb, remote::pgvector

You can use this distribution if you have GPUs and want to run an independent vLLM server container for running inference.

Environment Variables

The following environment variables can be configured:

• LLAMA_STACK_PORT: Port for the Llama Stack distribution server (default: 5001)
• INFERENCE_MODEL: Inference model loaded into the vLLM server (default: meta-llama/Llama-3.2-3B-Instruct)
• VLLM_URL: URL of the vLLM server with the main inference model (default: http://host.docker.internal:5100/v1)
• MAX_TOKENS: Maximum number of tokens for generation (default: 4096)
• SAFETY_VLLM_URL: URL of the vLLM server with the safety model (default: http://host.docker.internal:5101/v1)
• SAFETY_MODEL: Name of the safety (Llama-Guard) model to use (default: meta-llama/Llama-Guard-3-1B)

Setting up vLLM server

Please check the vLLM Documentation to get a vLLM endpoint. Here is a sample script to start a vLLM server locally via Docker:

export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0

docker run \
    --runtime nvidia \
    --gpus $CUDA_VISIBLE_DEVICES \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
    -p $INFERENCE_PORT:$INFERENCE_PORT \
    --ipc=host \
    vllm/vllm-openai:latest \
    --gpu-memory-utilization 0.7 \
    --model $INFERENCE_MODEL \
    --port $INFERENCE_PORT

If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like meta-llama/Llama-Guard-3-1B using a script like:

export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1

docker run \
    --runtime nvidia \
    --gpus $CUDA_VISIBLE_DEVICES \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
    -p $SAFETY_PORT:$SAFETY_PORT \
    --ipc=host \
    vllm/vllm-openai:latest \
    --gpu-memory-utilization 0.7 \
    --model $SAFETY_MODEL \
    --port $SAFETY_PORT

Running Llama Stack

Now you are ready to run Llama Stack with vLLM as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.

Via Docker

This method allows you to get started quickly without having to build the distribution code.

export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=5001

docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ./run.yaml:/root/my-run.yaml \
  llamastack/distribution-remote-vllm \
  --yaml-config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1

If you are using Llama Stack Safety / Shield APIs, use:

export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B

# You need a local checkout of llama-stack to run this, get it using
# git clone https://github.com/meta-llama/llama-stack.git
cd /path/to/llama-stack

docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  -v ./llama_stack/templates/remote-vllm/run-with-safety.yaml:/root/my-run.yaml \
  llamastack/distribution-remote-vllm \
  --yaml-config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 \
  --env SAFETY_MODEL=$SAFETY_MODEL \
  --env SAFETY_VLLM_URL=http://host.docker.internal:$SAFETY_PORT/v1

Via Conda

Make sure you have done uv pip install llama-stack and have the Llama Stack CLI available.

export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=5001

cd distributions/remote-vllm
llama stack build --template remote-vllm --image-type conda

llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env VLLM_URL=http://localhost:$INFERENCE_PORT/v1

If you are using Llama Stack Safety / Shield APIs, use:

export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B

llama stack run ./run-with-safety.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env VLLM_URL=http://localhost:$INFERENCE_PORT/v1 \
  --env SAFETY_MODEL=$SAFETY_MODEL \
  --env SAFETY_VLLM_URL=http://localhost:$SAFETY_PORT/v1