This article argues that local-AI performance on Macs depends on memory bandwidth, not CPU cores, GPU cores, or the Neural Engine. Using a simple formula (bandwidth ÷ model size × efficiency), it shows a 2021 M1 Max outperforms a 2024 M4 base chip by over 3× on a 7B model. It recommends buying used Max-tier machines and highlights lineup quirks like the M3 Pro’s bandwidth regression.

This article shows how macOS 27 includes a built-in LLM accessible via /usr/bin/fm that runs entirely offline and needs no account. The author tests it on an M1 MacBook Air, noting its limited memory, occasional inaccuracies, and a range of Terminal-based tricks.

The article breaks down which AI models and setups you can afford to run or train at home by 2026, comparing GPU costs, power use, and performance. It highlights efficient small-scale models, quantization tricks, and DIY hardware options to save money without sacrificing too much accuracy.

The author swaps ChatGPT Plus, Cursor and Midjourney for local AI on a 14″ MacBook Pro M5 Max. Two setups failed; a third ran locally by day nine and convinced him to re-subscribe.