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

Meta Reference Quantized Distribution

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self

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

API	Inference	Agents	Memory	Safety	Telemetry
Provider(s)	meta-reference-quantized	meta-reference	meta-reference, remote::pgvector, remote::chroma	meta-reference	meta-reference

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

Step 0. Prerequisite - Downloading Models

Please make sure you have llama model checkpoints downloaded in ~/.llama before proceeding. See installation guide here to download the models.

$ ls ~/.llama/checkpoints
Llama3.2-3B-Instruct:int4-qlora-eo8

Step 1. Start the Distribution

(Option 1) Start with Docker

$ cd distributions/meta-reference-quantized-gpu && docker compose up

Note

This assumes you have access to GPU to start a local server with access to your GPU.

Note

~/.llama should be the path containing downloaded weights of Llama models.

This will download and start running a pre-built docker container. Alternatively, you may use the following commands:

docker run -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./run.yaml:/root/my-run.yaml --gpus=all distribution-meta-reference-quantized-gpu --yaml_config /root/my-run.yaml

(Option 2) Start with Conda

1. Install the llama CLI. See CLI Reference

2. Build the meta-reference-quantized-gpu distribution

$ llama stack build --template meta-reference-quantized-gpu --image-type conda

3. Start running distribution

$ cd distributions/meta-reference-quantized-gpu
$ llama stack run ./run.yaml