Writing code models that run on 96 GB

70 models with published weights that fit in 96 GB — a MacBook Pro with 96. Counted at four-bit quantisation, weights only, leaving the machine about a third of its memory and a gigabyte for context: room for roughly 102 billion parameters. A mixture of experts is counted in full, because it is held in full even though only a few experts compute.

An Apple configuration for people who intend to run models rather than occasionally try one. Comfortably holds the seventy-billion class with a very long context, or a mixture-of-experts model whose active parameters are few but whose weights are all resident.

Models and agents for reading a codebase and changing it, rather than writing a snippet. The boards that matter here are the ones that run against real repositories — SWE-bench, SWE-rebench, Terminal-Bench — because a model can write a plausible function and still fail to make a test pass. Note whether what you are looking at is a model or an agent: an agent brings the harness, the file access and the loop, and is priced for it.

Wider

Writing code models Models that run on 96 GB