Running large language models on your own machine is now realistic for anyone with a modern GPU or an Apple Silicon Mac. Local inference keeps your data private, removes per-token costs, and works offline. The two most popular entry points are Ollama (CLI-first, open source) and LM Studio (a polished GUI).
Start by checking your available VRAM, pick a quantized model that fits, and run a small one first to confirm your setup. From there you can wire a local model into your own apps through an OpenAI-compatible API. This guide is updated as the tooling evolves.