Sachin Myadam

Posted on Jan 8

Running Llama 3 Locally with Apple MLX: A Guide to Unified Memory

#python #ai #apple #programming

I. Why Run Local LLMs?

For years, running large AI models meant paying for cloud GPUs or worrying about data privacy. That has changed.

Running LLMs locally on your own hardware solves three specific problems: latency, privacy, and cost. You don't need an internet connection, your data never leaves your device, and you stop paying per-token API fees. With Apple’s MLX framework, this is now practical on consumer hardware.

II. Context: From Cloud to Silicon

Machine learning used to require massive dedicated clusters. When deep learning took over with TensorFlow and PyTorch, the reliance on NVIDIA GPUs became the standard.

In 2020, Apple changed the architecture with the M1 chip and Unified Memory. In late 2023, they released MLX, a framework designed specifically for this architecture. It allows developers to run models efficiently without the overhead of traditional tools.

Meta’s open-source Llama series accelerated this shift. We saw the release of Llama 3 in 2024, followed by the major Llama 4 release in April 2025. These models are now efficient enough to run on a laptop while outperforming older server-grade models.

III. The Hardware: M3 Ultra and Unified Memory

The bottleneck in AI isn't always compute speed; often, it is memory bandwidth. Traditional PCs separate CPU RAM and GPU VRAM. To process a large model, you have to move data between them, which is slow.

Apple’s Unified Memory architecture lets the CPU and GPU access the same memory pool. The M3 Ultra, for example, supports up to 128GB of Unified Memory with a bandwidth of 819 GB/s. This allows you to load massive 70B or even quantized 405B parameter models directly into RAM, something that is impossible on most consumer dedicated GPUs.

January 2026 Update: While this guide focuses on the M3 Ultra for its massive memory bandwidth (819 GB/s), Apple’s new M5 architecture (released Oct 2025) brings even faster specialized Neural Accelerators. The good news? The same MLX principles and code provided below work perfectly on M5-powered MacBooks as well.

IV. Llama 3 Performance

Llama 3 remains a solid baseline for local development. It handles reasoning, coding, and multi-language tasks well. While newer models exist, the 8B version of Llama 3 is the perfect starting point for testing local inference because it fits comfortably in 8GB or 16GB of RAM.

V. Cloud vs. Local

Cloud GPUs like the H100 are still faster for massive training jobs. However, for inference (running the model), the MacBook Pro is surprisingly competitive. The main advantage is workflow: you can iterate on code, test prompts, and debug applications offline without waiting for server queues or managing API keys.

VI. Step-by-Step Tutorial

1. Install Dependencies

You need a Python environment (3.11+ is recommended). Open your terminal and run:

pip install mlx-lm

2. Run from CLI

To quickly test if the model works, use the command line interface. This pulls the 4-bit quantized version, which is optimized for speed and memory.

python -m mlx_lm.generate \
  --model mlx-community/Meta-Llama-3-8B-Instruct-4bit \
  --prompt "Write a Python script to sort a list of dictionaries."

3. Run with Python

For actual development, use this script:


from mlx_lm import load, generate

# Load the model and tokenizer
model, tokenizer = load("mlx-community/Meta-Llama-3-8B-Instruct-4bit")

# Generate a response
response = generate(
    model,
    tokenizer,
    prompt="Explain Unified Memory in one sentence.",
    verbose=True
)

print(response)

VII. What’s Next

MLX is still evolving. We are seeing better integration with the Neural Engine and support for more complex quantization methods. The focus for 2026 is on "quantized models"—making these large networks smaller without losing accuracy, so they run faster on standard laptops.

VIII. Final Thoughts

You no longer need a server farm to build AI applications. With Unified Memory and MLX, a MacBook is a legitimate platform for AI engineering. It’s cheaper, private, and capable of handling real-world production models.

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