MLX

MLX is an open-source array framework for machine learning developed by Apple's machine learning research team. It is designed specifically to run efficiently on Apple Silicon chips, including the M1, M2, M3, and M4 series with their unified memory architecture. Unlike CUDA or ROCm which target discrete GPUs, MLX leverages the Metal GPU and the Neural Engine in Apple's SoCs, providing a Python-first API similar to NumPy and PyTorch. For AI builders, MLX is important because it allows running LLMs locally on Macs with impressive performance, especially for models up to 7B-70B parameters, thanks to the large unified memory (up to 192GB on Mac Studio). MLX uses lazy computation and dynamic graph compilation, and it supports automatic differentiation, making it suitable for both inference and fine-tuning. The framework includes optimized kernels for attention, matrix multiplication, and quantization operations. MLX supports common quantization formats like MLX-specific 4-bit and 8-bit quantization, and tools like mlx-lm and llama.cpp with MLX backend enable deploying models like Llama, Mistral, and Phi. A key advantage over CUDA-based solutions is that MLX operates entirely on the Mac without needing a separate GPU, using the shared memory between CPU and GPU to eliminate data transfers. This unified memory approach can yield better performance for batch sizes that fit within system memory. MLX is still maturing, with fewer pre-built models and community tools compared to the CUDA ecosystem. It is most useful for developers who want to prototype or run LLMs on Mac without cloud costs.