RNN LM

Training compute

5.4×10¹⁶ FLOP

Parameters

70.3M

Published

Sep 26, 2010

RNN LM is an AI model developed by Johns Hopkins University (United States), first published in September 2010. 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 5.4×10¹⁶ FLOP of compute (estimation method: operation counting). The model has 70,265,000 parameters. It was trained on roughly 6.4M datapoints.

The reference paper has 6,038 citations. Epoch AI rates the confidence of this record as speculative.

Full record

Organization: Johns Hopkins University
Country of organization: United States
Domain: Language
Task: Language modeling
Training compute: 5.4×10¹⁶ FLOP
Compute estimation method: Operation counting
Parameters: 70,265,000
Dataset size: 6.4M
Training time: 504 h
Citations: 6,038
Epoch confidence: Speculative