VD-RHN

Training compute

3.6×10¹⁵ FLOP

Parameters

32M

Published

Jul 12, 2016

VD-RHN is an AI model developed by ETH Zurich and IDSIA (Switzerland), first published in July 2016. It works in the language domain, on tasks such as language modeling.

Training it took an estimated 3.6×10¹⁵ FLOP of compute (estimation method: operation counting). The model has 32,000,000 parameters. It was trained on roughly 929K datapoints.

Access: Unreleased. Its weights are not openly released. The reference paper has 493 citations. Epoch AI rates the confidence of this record as confident.

Full record

Organization: ETH Zurich, IDSIA
Country of organization: Switzerland
Domain: Language
Task: Language modeling
Training compute: 3.6×10¹⁵ FLOP
Compute estimation method: Operation counting
Parameters: 32,000,000
Dataset size: 929K
Model accessibility: Unreleased
Open weights: No
Citations: 493
Epoch confidence: Confident