# Running Gaia AI Nodes on Phala's Trusted Execution Environment

Here's how I got Gaia's LLaMA 3.2 running on Phala Cloud in just a few minutes.

## What is this all about?

**Gaia** is a decentralized AI network that lets you run AI models without relying on big tech companies. Think of it as your own personal AI assistant that you actually control.

**Phala Network** provides something called a Trusted Execution Environment (TEE). This is basically a secure computing space where your code runs privately - even the cloud provider can't peek at what you're doing.

When you combine them, you get a private AI node that's actually yours.

## Why does this matter?

- **Privacy**: Your AI conversations stay private
- **Control**: You own your AI, not some corporation
- **Decentralization**: No single point of failure
- **Censorship resistance**: Nobody can shut down your AI

## How to deploy your own

It's surprisingly simple. Here's what worked for me:

### Step 1: Get your docker-compose.yml ready

services:
  llama:
    image: thenocodeguyonline/llama-3.2
    ports:
      - 8080:8080
    volumes:
      - /var/run/tappd.sock:/var/run/tappd.sock
    environment:
      - GRANT_SUDO=yes
    user: root

### Step 2: Deploy on Phala Cloud

1. Go to Phala Cloud
2. Create a new deployment
3. Upload your docker-compose.yml
4. Hit deploy
5. Wait a few minutes

That's it. Seriously.

## What happens next?

Once deployed, you'll have your own AI node running inside a secure TEE. You can:

- Chat with your AI privately
- Use it for coding, writing, or research
- Know that your data isn't being harvested
- Access it from anywhere

## The bigger picture

This isn't just about running one AI model. It's about building a future where AI is decentralized and private by default. Every node you deploy makes the network stronger and more resilient.

Plus, you're not at the mercy of OpenAI's rate limits or Google's content policies. Your AI, your rules.

## Try it yourself

The whole process took me less than 10 minutes from start to finish. If you've ever deployed a Docker container, you can do this.

The future of AI is decentralized. Why not be part of it?

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*Want to get started? Head over to Phala Cloud and give it a try. The docker-compose.yml above is all you need.*