Reasoning models that run on 32 GB

54 models with published weights that fit in 32 GB — a well-specified laptop. Counted at four-bit quantisation, weights only, leaving the machine about a third of its memory and a gigabyte for context: room for roughly 33 billion parameters. A mixture of experts is counted in full, because it is held in full even though only a few experts compute.

Enough for a thirty-billion-parameter model at four-bit with a long context, or a smaller one at higher precision if quality matters more than size. A practical ceiling for a laptop that also has to be a laptop.

Models that plan before they answer, spending extra tokens on working the problem through. They win on mathematics, hard code and anything with several steps, and they are measured on boards like GPQA, AIME and ARC-AGI. The catch is what the thinking costs: reasoning is billed as output tokens, so the same model can cost several times more per answer at a high effort than a low one, and for a question that needed one turn you have paid for a monologue.

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Reasoning models Models that run on 32 GB