MLX

MLX is Apple's open-source machine learning framework, released in late 2023 and specifically optimized for Apple Silicon (M1, M2, M3, M4 series). Unlike llama.cpp or ExLlamaV2 which use cross-platform approaches, MLX is built exclusively for Apple's hardware and takes advantage of its unified memory architecture and Neural Engine.

Why MLX Matters for Apple Silicon

Apple Silicon's unified memory is shared between the CPU and GPU with high bandwidth. MLX is designed to exploit this architecture directly, using lazy computation graphs and efficient memory allocation to maximize throughput on M-series chips.

In practice, MLX achieves 10–30% higher inference speeds than llama.cpp's Metal backend on the same Apple Silicon hardware, particularly for larger models (32B+) where memory bandwidth utilization matters most.

mlx-lm

The mlx-lm Python package is the primary interface for running LLMs with MLX. It supports downloading models from Hugging Face in MLX format, quantizing models locally, and running inference with a command-line or Python API.

Common usage:

pip install mlx-lm
python -m mlx_lm.generate --model mlx-community/Llama-3.1-70B-Instruct-4bit --prompt "Explain transformers"

The mlx-community organization on Hugging Face maintains a large collection of pre-converted MLX models.

MLX Format vs. GGUF

MLX uses its own model format (safetensors-based with MLX quantization). GGUF models used by llama.cpp and Ollama cannot be loaded directly by MLX. Both formats support similar quantization levels, but require separate downloads or conversion.

When MLX Outperforms llama.cpp

For Apple Silicon users, MLX is generally faster than llama.cpp's Metal backend for models in the 32B–70B range. For 7B models, the difference is smaller because smaller models are less bandwidth-constrained.

For Mac users with 64GB or 128GB unified memory looking to run 70B models, MLX is the recommended inference path for maximum throughput.