How to Run Local LLMs on Apple Silicon: Tools, Formats and Key Tradeoffs

Running large language models locally on Apple Silicon involves choosing between several tools and model formats, each with distinct trade-offs. The main options are llama.cpp, Apple's MLX framework, Ollama, and LM Studio, which differ in speed, compatibility, and ease of use. Since Ollama 0.19 in March 2026, Macs with 32GB or more of unified memory now use Apple's MLX framework as the inference backend, delivering notable speed gains over the older llama.cpp path. Model format choice also matters: GGUF offers broader compatibility and a larger library of conversions, while MLX-format models tend to run faster on Apple Silicon when a conversion is available. Users should reserve roughly 20–25% of unified memory for the operating system before allocating the remainder to model weights and cache, since macOS and models share the same memory pool.