Evaluation models that run on 24 GB

14 models with published weights that fit in 24 GB — a 24 GB card, or a Mac with 24. Counted at four-bit quantisation, weights only, leaving the machine about a third of its memory and a gigabyte for context: room for roughly 24 billion parameters. A mixture of experts is counted in full, because it is held in full even though only a few experts compute.

A 24 GB graphics card or a Mac configured with 24. This is the first size where the well-regarded mid-weight models — the twenty-something billion parameter class — run comfortably, and where a local model starts to be a real alternative to an API for daily work rather than a demonstration.

Tools for judging what a model produced: scoring runs, tracing a chain of calls, catching a regression before a user does. Priced per trace or per evaluation. Most use a model as the judge, which is worth knowing, because it means your evaluation has a bill and an opinion of its own.

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Evaluation models Models that run on 24 GB