MLX

MLX is Apple's high-performance array framework for machine learning on Apple silicon, leveraging unified memory for zero-copy efficiency.

MLX is an open-source array framework from Apple machine learning research, purpose-built for efficient ML on Apple Silicon (M-series chips). Its core strength is the unified memory model: this eliminates costly data transfers between the CPU and GPU, a major performance bottleneck in traditional frameworks. The API is immediately familiar, closely mirroring NumPy for array operations and PyTorch for higher-level packages like `mlx.nn` and `mlx.optimizers`. It supports Python, C++, C, and Swift bindings, making it highly flexible. Researchers use MLX to quickly train and deploy complex models, with examples including large-scale text generation with LLaMA and image creation via Stable Diffusion.

https://mlx-framework.org