Worked examples of machines for local AI at different price points. Each is a starting point with its trade-offs stated plainly, not a definitive shopping list.
Not everyone who wants to run local AI on a Mac needs a Studio. A Mac mini with the higher memory option holds mid-sized models quietly and at low power, and costs far less than the large-model Apple Silicon machines.
For many people the best value in local AI is a used RTX 3090. Its 24GB runs models up to around 32B at a 4-bit quantisation, often for less than a new 16GB card, if you are comfortable buying second-hand.
A quiet, low-power route to running large models at home, built around a Mac Studio with plenty of unified memory. Slower generation than a discrete GPU, but able to hold models that a single consumer card cannot.
A first local AI machine built around a 16GB card, enough to run 7B to 14B models comfortably without spending flagship money. The sensible way to find out whether local AI suits you before committing more.
Image generation asks different things of a machine than a language model. A fast 16GB card handles SDXL and FLUX comfortably, and prioritises the bandwidth and compute that keep generation quick.
A compact, low-power route to running very large models, built around a Ryzen AI Max+ 395 mini PC with 128GB of unified memory. It holds models no single consumer GPU can, in exchange for slower generation.
A no-compromises single-card build around the RTX 5090. Its 32GB and high bandwidth run strong models up to around 32B quickly, with room for longer context than a 24GB card allows. Fast, capable, and expensive.
A single 48GB professional card runs 70B-class models on one GPU, with no multi-card complexity. It is expensive, but for a quiet, reliable workstation that holds large models, it is the cleanest option short of a server.