Embeddings models that run on 96 GB

60 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 that turn text into a vector so it can be searched by meaning rather than by words. They are cheap — usually cents per million tokens, and often priced for input only, since nothing comes back but numbers. Two things decide the choice: the dimension of the vector, which sets what your database will cost to hold, and whether the model was trained for your language. Changing model later means re-embedding everything you have.

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Embeddings models Models that run on 96 GB