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# Starting a Llama Stack
# 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](importing_as_library)
**Docker**:
Another simple way to start interacting with Llama Stack is to just spin up docker which is pre-built with all the providers you need. We provide a number of pre-built Docker containers so you can start a Llama Stack server instantly. You can also build your own custom Docker container. Which distribution to choose depends on the hardware you have. See [Selection of a Distribution](distributions/selection) for more details.
**Conda**:
Lastly, if you have a custom or an advanced setup or you are developing on Llama Stackyou 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](building_distro) for more details.
```{toctree}
:maxdepth: 3
:maxdepth: 1
:hidden:
importing_as_library
building_distro
configuration
```
You can instantiate a Llama Stack in one of the following ways:
- **As a Library**: this is the simplest, especially if you are using an external inference service. See [Using Llama Stack as a Library](importing_as_library)
- **Docker**: we provide a number of pre-built Docker containers so you can start a Llama Stack server instantly. You can also build your own custom Docker container.
- **Conda**: finally, you can build a custom Llama Stack server using `llama stack build` containing the exact combination of providers you wish. We have provided various templates to make getting started easier.
Which templates / distributions to choose depends on the hardware you have for running LLM inference.
- **Do you have access to a machine with powerful GPUs?**
If so, we suggest:
- {dockerhub}`distribution-remote-vllm` ([Guide](self_hosted_distro/remote-vllm))
- {dockerhub}`distribution-meta-reference-gpu` ([Guide](self_hosted_distro/meta-reference-gpu))
- {dockerhub}`distribution-tgi` ([Guide](self_hosted_distro/tgi))
- {dockerhub} `distribution-nvidia` ([Guide](self_hosted_distro/nvidia))
- **Are you running on a "regular" desktop machine?**
If so, we suggest:
- {dockerhub}`distribution-ollama` ([Guide](self_hosted_distro/ollama))
- **Do you have an API key for a remote inference provider like Fireworks, Together, etc.?** If so, we suggest:
- {dockerhub}`distribution-together` ([Guide](self_hosted_distro/together))
- {dockerhub}`distribution-fireworks` ([Guide](self_hosted_distro/fireworks))
- **Do you want to run Llama Stack inference on your iOS / Android device** If so, we suggest:
- [iOS SDK](ondevice_distro/ios_sdk)
- [Android](ondevice_distro/android_sdk)
- **Do you want a hosted Llama Stack endpoint?** If so, we suggest:
- [Remote-Hosted Llama Stack Endpoints](remote_hosted_distro/index)
You can also build your own [custom distribution](building_distro).