How to Run Llama 3 by Meta AI Locally

Meta’s Llama 3 is one of the most powerful open-source large language models you can run on your own hardware. But how do you actually get it working on your laptop or PC—without the guesswork? Here’s the no-nonsense guide to getting Llama 3 up and running locally.

What You’ll Need

• A computer with at least 16GB RAM (32GB+ recommended for bigger models)
• A decent CPU (Llama 3 can run on CPU, but it’s faster with a good GPU)
• Python 3.8 or newer
• Basic command line skills
• About 15-50GB of free disk space (depending on model size)

1. Download the Llama 3 Weights

First, you’ll need the official Llama 3 weights from Meta. Meta requires you to request access for download.

• Go to Meta’s Llama 3 Request Form
• Fill out the form with your information.
• Wait for approval (can take a few hours or days).
• Download the model files when you get access.

Note: You’ll get multiple sizes (e.g., 8B, 70B parameters). For most local machines, start with the smaller (8B) model.

2. Set Up Your Environment

Install Python and Git

Most systems already have Python, but make sure it’s up to date:

python3 --version

If you need to, download Python here.

Install Git:

# macOS
brew install git
# Ubuntu/Debian
sudo apt-get install git
# Windows
Download from [git-scm.com](https://git-scm.com/)

Create a Virtual Environment

Open your terminal and run:

python3 -m venv llama3env
source llama3env/bin/activate  # On Windows: llama3env\Scripts\activate

Install Required Packages

You’ll need the transformers library from Hugging Face (plus a few helpers):

pip install torch torchvision torchaudio
pip install transformers accelerate

3. Convert and Load the Model

Llama 3’s weights might come in a format best handled by Hugging Face’s Transformers library. Here’s how to load it:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "/path/to/llama-3"  # Where you saved the model weights

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto")

Or, if the weights are already on Hugging Face (and you have access):

model_id = "meta-llama/Meta-Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")

4. Run Llama 3 Locally

Here’s a minimal script to generate text:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "path_or_hf_model_id"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)

prompt = "What is the future of AI?"

inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Save this as run_llama3.py and run:

python run_llama3.py

5. Optional: Use a Chat UI

You don’t have to use the command line. You can try a friendly local web UI like Ollama or Text Generation WebUI. These tools let you interact with Llama 3 in your browser.

• Ollama — the easiest way to run Llama 3 (just ollama run llama3).
• Text Generation WebUI — more advanced features and chat options.

Troubleshooting Tips

• Out of Memory? Try a smaller model, or use torch_dtype=torch.float16 to save RAM.
• Slow? CPU is much slower than GPU. For best results, use a machine with an NVIDIA GPU.
• Access Denied? Double-check your Meta and Hugging Face permissions.

Wrapping Up

Running Llama 3 locally isn’t rocket science—but you do need to follow the steps and make sure your machine is ready. Once it’s set up, you’ll have a world-class AI model at your fingertips, running securely and privately on your own hardware.

Need more help? Drop your questions in the comments, and I’ll keep this post updated with new info!

Mark Vincent

Tech enthusiast and content creator passionate about making technology simple for everyone. I share practical tips, guides, and reviews on the latest in computers, software, and gadgets. Let’s explore the digital world together!