AI models on M4, 24 GB

A Mac with an M4 and 24 GB of memory runs 28 of these 44 models well. The largest chat model that runs well is K2-Horizon 36B. The best-rated model that runs well here is Gemma 4 26B at Q4_K_S, which writes about 14 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 iMac 24-inch, Mac mini, MacBook Pro 14-inch, MacBook Air 13-inch and MacBook Air 15-inch (2024 to 2025), with a 8-core or 10-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 24 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 (120 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.