Local LLM Inference in 2026: The Complete Guide to Tools, Hardware & Open-Weight Models

TL;DR: Ollama is the fastest path to running local LLMs (one command to install, one to run). The Mac Mini M4 Pro 48GB (~$1,999) is the best-value hardware. Q4_K_M is the sweet spot quantization for most users. Open-weight models like GLM-5, MiniMax M2, and Hermes 4 are impressively capable for a wide range of tasks. This guide covers 10 inference tools, every quantization format, hardware at every budget, and the builders making all of this possible. I've been setting up local inference on my own hardware recently — an M4 Pro Mac Mini running Ollama — and I wanted to compile everything I've learned into one place. This guide is as much for my own reference as it is for anyone else exploring this space. The tooling in 2026 has matured to the point where a $600 Mac Mini can run 14B parameter models and a $1,600 setup handles 70B. Whether you want to reduce API costs for simple tasks, keep sensitive data private, build offline-capable apps, or just understand how these models actually wo