MLX (Apple)

MLX is Apple's open-source array framework optimized for Apple Silicon. The Apple equivalent of PyTorch + CUDA, with first-party Metal kernels and Apple Neural Engine integration. Two key surfaces: MLX-LM (the Python LLM-inference library) and MLX Swift (the iOS/macOS native bindings used in App Store-shipping apps).

What MLX does well: unified-memory aware model loading (no GPU/CPU copy overhead on Apple Silicon), ANE delegation for compatible ops, native quantization formats (MLX-4bit, MLX-8bit) tuned for Apple Silicon's memory bandwidth. Tok/s on M3 Max / M3 Ultra is competitive with consumer NVIDIA at the same VRAM tier when the workload is bandwidth-bound (most LLM inference is).

What MLX doesn't do: cross-platform deployment (it's Apple-only), CUDA quant formats (no AWQ/GPTQ/EXL2 — convert to MLX format first), full PyTorch ecosystem parity. The model coverage is good but lags Hugging Face mainline by 2-6 weeks for new architectures. For Apple Silicon production deployments, MLX-LM is the operator default; for cross-platform Mac+Linux+Windows deployments, llama.cpp Metal is the more portable fallback.