09ed0e9c9f
This PR moves some content from [the recent blog post](https://blog.vllm.ai/2025/01/27/intro-to-llama-stack-with-vllm.html) to here as a more official guide for users who'd like to deploy Llama Stack on Kubernetes. --------- Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
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Starting a Llama Stack Server
You can run a Llama Stack server in one of the following ways:
As a Library:
This is the simplest way to get started. Using Llama Stack as a library means you do not need to start a server. This is especially useful when you are not running inference locally and relying on an external inference service (eg. fireworks, together, groq, etc.) See Using Llama Stack as a Library
Container:
Another simple way to start interacting with Llama Stack is to just spin up a container (via Docker or Podman) which is pre-built with all the providers you need. We provide a number of pre-built images so you can start a Llama Stack server instantly. You can also build your own custom container. Which distribution to choose depends on the hardware you have. See Selection of a Distribution for more details.
Conda:
If you have a custom or an advanced setup or you are developing on Llama Stack you can also build a custom Llama Stack server. Using llama stack build and llama stack run you can build/run a custom Llama Stack server containing the exact combination of providers you wish. We have also provided various templates to make getting started easier. See Building a Custom Distribution for more details.
Kubernetes:
If you have built a container image and want to deploy it in a Kubernetes cluster instead of starting the Llama Stack server locally. See Kubernetes Deployment Guide for more details.
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importing_as_library
building_distro
configuration
selection
kubernetes_deployment