Running local LLMs on Apple Silicon Macs (M1, M2, M3) is now a viable workflow thanks to unified memory architecture (UMA), which eliminates discrete GPU VRAM limits. This guide covers hardware tier benchmarks across all Apple Silicon chips, setup for Ollama, llama.cpp, and MLX frameworks, Metal GPU acceleration, quantization strategy (Q4_K_M through Q8_0), memory management, context/batch tuning, and macOS system optimization. Key insight: Apple Silicon's advantage is memory capacity for large models (70B+), while NVIDIA leads in raw compute density for models that fit in VRAM.
Table of contents
Table of ContentsPrerequisitesWhy Apple Silicon Suits Local LLM InferenceHardware Tier Breakdown: M1 vs. M2 vs. M3 for LLMsSetting Up Your Local LLM EnvironmentNeural Engine vs. GPU vs. CPU: Understanding Execution PathsMemory Optimization and ConfigurationAdvanced Optimization TechniquesPractical Performance ExpectationsKey RecommendationsSort: