SNM-skip

Training compute

3×10²⁰ FLOP

Parameters

62B

Published

Dec 3, 2014

SNM-skip is an AI model developed by Google (United States), first published in December 2014. It works in the language domain, on tasks such as language modeling. It counts among the frontier models: the systems trained with the most compute of their moment.

Training it took an estimated 3×10²⁰ FLOP of compute (estimation method: operation counting). The model has 62,000,000,000 parameters. It was trained on roughly 800M datapoints.

The reference paper has 14 citations. Epoch AI rates the confidence of this record as speculative.

Full record

Organization: Google
Country of organization: United States
Domain: Language
Task: Language modeling
Training compute: 3×10²⁰ FLOP
Compute estimation method: Operation counting
Parameters: 62,000,000,000
Dataset size: 800M
Citations: 14
Epoch confidence: Speculative