GPU DBNs

Training compute

10¹⁵ FLOP

Parameters

100M

Published

Jun 15, 2009

GPU DBNs is an AI model developed by Stanford University (United States), first published in June 2009. It works in the other domain, on tasks such as miscellaneous image analysis.

Training it took an estimated 10¹⁵ FLOP of compute (estimation method: hardware). The model has 100,000,000 parameters. It was trained on roughly 121.3B datapoints.

The reference paper has 1,032 citations. Epoch AI rates the confidence of this record as confident.

Full record

Organization: Stanford University
Country of organization: United States
Domain: Other
Task: Miscellaneous image analysis
Training compute: 10¹⁵ FLOP
Compute estimation method: Hardware
Parameters: 100,000,000
Dataset size: 121.3B
Numerical format: FP32
Citations: 1,032
Epoch confidence: Confident