Summary. I explore how the MLX framework optimizes the performance of large language models on Apple Silicon. I demonstrate that MLX provides significant speed and memory advantages over PyTorch by leveraging unified memory and compiled training steps.

What the posts say

MLX is a framework designed for Apple silicon.

“MLX is an array framework designed for efficient and flexible machine learning research on Apple silicon.” — MLX 1

The MLX engine provides a significant speed advantage when the training step is compiled.

“With it, MLX is about a quarter faster than itself, and 16% faster than PyTorch.” — MiniGPT (Part 4)

MLX uses less memory than PyTorch for the exact same machine and task.

“MLX uses less memory either way: 3.7 to 3.9 GB against PyTorch's 4.6 GB, about 15 to 20% less for exactly the same machine.” — MiniGPT (Part 4)

MLX avoids memory copying because Apple chips share a single pool of memory.

“MLX is built around that, so there is nothing to copy and no .to(device) anywhere: the training batch goes straight into an MLX array, and the GPU reads it where it is.” — MiniGPT (Part 4)

Lazy evaluation allows MLX to see the full list of steps before it starts.

“That sounds like a delay, but it gives MLX the whole list of steps before it starts, so it can see which ones can be done together, and which ones are never needed at all.” — MiniGPT (Part 4)

The results from both the PyTorch and MLX versions are nearly identical.

“But they should end up in the same place, and they do: after 3,000 steps, the MLX machine scores 0.689 bits per byte on the test stories, and the PyTorch machine from Part 3 scores 0.697.” — MiniGPT (Part 4)

I used a specific python command to fuse the model.

“I ran python -m mlx_lm fuse to create the fused model” — MLX 1

I converted the model to GGUF format for compatibility.

“I ran convert_hf_to_gguf.py to convert the fused model to GGUF format” — MLX 1