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docs/source/getting_started/index.md

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+# Getting Started with Llama Stack
+
+At the end of the guide, you will have learnt how to:
+- get a Llama Stack server up and running
+- get a agent (with tool-calling, vector stores) which works with the above server
+
+To see more example apps built using Llama Stack, see [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main).
+
+## Starting Up Llama Stack Server
+
+### Decide your
+There are two ways to start a Llama Stack:
+
+- **Docker**: we provide a number of pre-built Docker containers allowing you to get started instantly. If you are focused on application development, we recommend this option.
+- **Conda**: the `llama` CLI provides a simple set of commands to build, configure and run a Llama Stack server containing the exact combination of providers you wish. We have provided various templates to make getting started easier.
+
+Both of these provide options to run model inference using our reference implementations, Ollama, TGI, vLLM or even remote providers like Fireworks, Together, Bedrock, etc.
+
+### Decide Your Inference Provider
+
+Running inference of the underlying Llama model is one of the most critical requirements. Depending on what hardware you have available, you have various options:
+
+- **Do you have access to a machine with powerful GPUs?**
+If so, we suggest:
+  - `distribution-meta-reference-gpu`:
+    - [Docker]()
+    - [Conda]()
+  - `distribution-tgi`:
+    - [Docker]()
+    - [Conda]()
+
+- **Are you running on a "regular" desktop machine?**
+If so, we suggest:
+  - `distribution-ollama`:
+    - [Docker]()
+    - [Conda]()
+
+- **Do you have access to a remote inference provider like Fireworks, Togther, etc.?** If so, we suggest:
+  - `distribution-fireworks`:
+    - [Docker]()
+    - [Conda]()
+  - `distribution-together`:
+    - [Docker]()
+    - [Conda]()
+
+## Testing with client
+Once the server is setup, we can test it with a client to see the example outputs by . This will run the chat completion client and query the distribution’s `/inference/chat_completion` API. Send a POST request to the server:
+
+```
+curl http://localhost:5000/inference/chat_completion \
+-H "Content-Type: application/json" \
+-d '{
+    "model": "Llama3.1-8B-Instruct",
+    "messages": [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Write me a 2 sentence poem about the moon"}
+    ],
+    "sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512}
+}'
+
+Output:
+{'completion_message': {'role': 'assistant',
+  'content': 'The moon glows softly in the midnight sky, \nA beacon of wonder, as it catches the eye.',
+  'stop_reason': 'out_of_tokens',
+  'tool_calls': []},
+ 'logprobs': null}
+
+```
+
+Check out our client SDKs for connecting to Llama Stack server in your preferred language, you can choose from [python](https://github.com/meta-llama/llama-stack-client-python), [node](https://github.com/meta-llama/llama-stack-client-node), [swift](https://github.com/meta-llama/llama-stack-client-swift), and [kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) programming languages to quickly build your applications.
+
+You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo.
+
+
+```{toctree}
+:hidden:
+:maxdepth: 2
+
+developer_cookbook
+distributions/index
+```