llama-stack/docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md

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Meta Reference Quantized Distribution

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self

The llamastack/distribution-meta-reference-quantized-gpu distribution consists of the following provider configurations:

API	Provider(s)
agents	inline::meta-reference
datasetio	remote::huggingface, inline::localfs
eval	inline::meta-reference
inference	inline::meta-reference-quantized
memory	inline::faiss, remote::chromadb, remote::pgvector
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::memory-runtime, remote::model-context-protocol

The only difference vs. the meta-reference-gpu distribution is that it has support for more efficient inference -- with fp8, int4 quantization, etc.

Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.

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 Meta Reference server (default: meta-llama/Llama-3.2-3B-Instruct)
• INFERENCE_CHECKPOINT_DIR: Directory containing the Meta Reference model checkpoint (default: null)

Prerequisite: Downloading Models

Please make sure you have llama model checkpoints downloaded in ~/.llama before proceeding. See installation guide here to download the models. Run llama model list to see the available models to download, and llama model download to download the checkpoints.

$ ls ~/.llama/checkpoints
Llama3.1-8B           Llama3.2-11B-Vision-Instruct  Llama3.2-1B-Instruct  Llama3.2-90B-Vision-Instruct  Llama-Guard-3-8B
Llama3.1-8B-Instruct  Llama3.2-1B                   Llama3.2-3B-Instruct  Llama-Guard-3-1B              Prompt-Guard-86M

Running the Distribution

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.

LLAMA_STACK_PORT=5001
docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  llamastack/distribution-meta-reference-quantized-gpu \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct

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

docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  llamastack/distribution-meta-reference-quantized-gpu \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
  --env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B

Via Conda

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

llama stack build --template meta-reference-quantized-gpu --image-type conda
llama stack run distributions/meta-reference-quantized-gpu/run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct

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

llama stack run distributions/meta-reference-quantized-gpu/run-with-safety.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
  --env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B