What is MLX and how does it optimize AI workloads on Apple Silicon?
MLX is Apple open-source machine learning framework designed specifically for Apple Silicon. It leverages the unified memory architecture to eliminate data copying between CPU and GPU, providing lazy evaluation that only computes values when needed. MLX offers a NumPy-like API with automatic differentiation, making it familiar to ML practitioners. It supports dynamic shapes, JIT compilation via Metal shaders, and distributed training across multiple devices. MLX provides pre-built model implementations for transformers, diffusion models, and other architectures. For offline AI on Mac, MLX achieves significantly better performance than PyTorch MPS for many workloads. The mlx-lm package provides optimized LLM inference, and mlx-whisper handles speech-to-text, both running natively on Apple hardware.