MLX

MLX is Apple's answer to "why can't I train models efficiently on my MacBook?" It's an array framework built specifically for Apple Silicon's unified memory architecture — no copying data between CPU and GPU. If you have an M-series Mac, MLX squeezes out performance that PyTorch's MPS backend can't match for inference.

For indie hackers running local LLMs on a MacBook Pro, MLX is the fastest path. It ships with mlx-lm for running Llama, Mistral, and other models locally. PyTorch is the cross-platform standard but wastes cycles on Apple's non-CUDA architecture. llama.cpp is the other local inference option with broader hardware support.

The catch: MLX is Apple Silicon only — your code won't run on Linux, Windows, or NVIDIA GPUs. That means no cloud deployment, no team members on non-Mac hardware, and no GPU cluster training. PyTorch is still faster for training (MLX wins on inference). The ecosystem is tiny compared to PyTorch's. Use MLX for local experimentation and inference; use PyTorch for anything that needs to run beyond your laptop.