Llama 3 Local Setup: Prerequisites and System Setup

This guide will walk you through preparing your system to run Llama 3.1 8B locally. We'll focus on setting up the necessary environment and ensuring your system meets all requirements.

System Requirements

Before beginning, ensure your system meets these minimum specifications:

• NVIDIA GPU with at least 8GB VRAM (guide optimized for RTX 4070)
• Ubuntu operating system
• Python 3.8 or newer
• At least 16GB system RAM
• 20GB free disk space

Installing System Dependencies

First, we'll install the necessary system packages. Open your terminal and run:

# Update package list
sudo apt update

# Install required packages
sudo apt install python3-venv git git-lfs -y

# Initialize Git LFS (Large File Storage)
git lfs install

Setting Up Python Environment

We'll create a dedicated virtual environment for our Llama 3 project:

# Create project directory
mkdir ~/llama3_project
cd ~/llama3_project

# Create subdirectories
mkdir models webui

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate

# Upgrade pip
pip install --upgrade pip

Installing CUDA and PyTorch

For optimal performance, we need to install PyTorch with CUDA support:

# Install PyTorch with CUDA support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

# Install additional dependencies
pip install transformers accelerate bitsandbytes

Verifying GPU Setup

To ensure your GPU is properly recognized and configured:

1. Check NVIDIA driver installation:

nvidia-smi

You should see output showing your GPU (RTX 4070) and driver version.

2. Verify CUDA availability in Python:

import torch
print("CUDA available:", torch.cuda.is_available())
print("CUDA version:", torch.version.cuda)

Environment Variables (Optional)

For optimal performance, you can set these environment variables:

# Add to your ~/.bashrc file
export CUDA_VISIBLE_DEVICES=0
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512

Troubleshooting Common Issues

CUDA Not Found

If you see "CUDA not available" errors:

1. Verify NVIDIA drivers are installed
2. Ensure CUDA toolkit is properly installed
3. Reinstall PyTorch with the correct CUDA version

Out of Memory Errors

If you encounter memory issues:

1. Close unnecessary applications
2. Reduce browser tabs
3. Consider increasing swap space

Python Version Conflicts

If you encounter Python version issues:

1. Verify Python version: python --version
2. Create a new virtual environment with the correct Python version
3. Reinstall dependencies in the new environment

Next Steps

Now that your system is prepared, you can proceed to:

1. Download the Llama 3 model
2. Install the text-generation-webui interface
3. Configure and optimize the model for your system

In the next guide, we'll cover downloading and installing the Llama 3 model and setting up the web interface.

Best Practices

1. Regular Updates: Keep your NVIDIA drivers and CUDA toolkit updated
2. Resource Monitoring: Use nvidia-smi to monitor GPU usage
3. Clean Environment: Use virtual environments for different projects
4. Backup: Back up your configuration files and model files

By following this guide, you've created a solid foundation for running Llama 3 locally. Make sure all components are properly installed before proceeding to the model installation in the next guide.