AI models on M1, 8 GB

A Mac with an M1 and 8 GB of memory runs 9 of these 44 models well. The largest chat model that runs well is Gemma 4 12B. The best-rated model that runs well here is Gemma 3n E4B at UD-Q2_K_XL, which writes about 8.8 words a second 4K tokens into a chat. Every number here is an estimate from measured llama.cpp tables, not yet a measurement from this Mac.

Sold as MacBook Air 13-inch, MacBook Pro 13-inch, Mac mini and iMac 24-inch (2020 to 2021), with a 7-core or 8-core GPU.

Fit is the app's own verdict: the memory a size needs at each context length, measured by llama.cpp's calculator when the catalog was built, against this Mac's 8 GB and the share of it the graphics side may use. Runs great and Runs well leave room for the rest of the Mac. Tight fit means quit other large apps first.

Speed is predicted from the chip's memory bandwidth (68 GB/s here), the weights a token reads, a fixed cost per layer, and the cost of attention over the chat so far. The constants were fitted on October 11, 2026 to 108 measured llama-bench rows across every Apple chip, and land within about 5% of those rows. Two independent reports checked in October 2026 ran 14% and 18% faster at an empty chat, so the estimate leans low. A mixture-of-experts model and any chip newer than M5 are extrapolated.

Outmake times every model you run. With Send Measurements and Errors on, your Mac's numbers join a public table and replace these estimates for everyone with the same Mac. The method, the rules and the data.