Apple Silicon LLM Inference Optimization: The Complete Guide to Maximum Performance

TL;DR: MLX is 20-87% faster than llama.cpp for generation on Apple Silicon (under 14B params). Use Ollama 0.19+ with the MLX backend for 93% faster decode with zero config. Q4_K_M is the sweet spot quantization (3.3% quality loss, 75% size reduction). On a 32GB Mac, top picks include Qwen 3.5 9B (daily driver), DeepSeek R1 Distill 14B (reasoning), Qwen 3.5 35B-A3B (MoE), and OpenAI gpt-oss-20b — but the "best" model depends on your use case, context length needs, and quality tolerance. Memory bandwidth is your bottleneck — not compute, not VRAM, not GPU cores. This guide covers every optimization that matters. I run a 32GB M4 Mac Mini as my local inference box. After weeks of benchmarking different engines, quantization levels, models, and optimization techniques, I've compiled everything into one reference. The Apple Silicon inference ecosystem has matured dramatically in 2026 — MLX is no longer experimental, Ollama ships an MLX backend, and vLLM has two competing Apple Silicon ports.