MLX

MLX is Apple's machine learning framework, built first for Apple Silicon. It exploits unified memory (on M series chips the CPU and GPU share the same RAM) so tensors never get copied across a bus the way they do between a host and an NVIDIA card. The API deliberately mirrors NumPy and PyTorch, so if you know either you already know most of this.

The story changed this year. MLX now ships a CUDA backend and a CPU only build for Linux, installed with pip install mlx[cuda] or mlx[cpu]. Training on a Mac and deploying to a Linux box with NVIDIA hardware is no longer a rewrite. Apple Silicon is still where it runs fastest and gets the most testing, and the CUDA path is newer, so verify the specific operations you depend on before you bet a production pipeline on it.

MIT licensed, maintained by Apple's machine learning research group, and moving fast. Solo developers on a Mac: this is the best way to run and fine tune models on hardware you already own. Small teams doing local experimentation: use it, and keep PyTorch for anything shipping to a mixed fleet. Large teams with existing CUDA infrastructure: treat MLX as an excellent front end for Mac dev machines rather than a replacement for your training stack.

The catch: the ecosystem is still a fraction of PyTorch's. Most papers, most tutorials, and most pretrained weights assume torch, so you will spend time porting or waiting for someone else to port. ml-explore/mlx-lm covers the common language model cases and the coverage thins out quickly past that.