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

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# Remote vLLM Distribution
```{toctree}
: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`, `inline::sentence-transformers` |
| 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`, `remote::wolfram-alpha` |
| 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](https://docs.vllm.ai/en/v0.5.5/serving/deploying_with_docker.html) to get a vLLM endpoint. Here is a sample script to start a vLLM server locally via Docker:

```bash
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:

```bash
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.

```bash
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:

```bash
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.

```bash
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:

```bash
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
```