Parameters
Concept
Core Technical
The learned numbers — and why 'bigger' stopped being the whole story.
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Parameters are the weights a model learns; storing and moving them is what all the memory on this site is for (a 405B-parameter model needs ~800 GB at 16-bit). Counts climbed from GPT-2's 1.5B to trillion-parameter mixtures, but since Chinchilla the field optimizes parameters against data and compute jointly — and closed labs stopped disclosing counts at all, which is why so many records here say 'not publicly disclosed.'
How this connects to the rest of the stack
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Related
Sources
Record reviewed as of 2026-01-15 — see Methodology.