JEST++

Training compute

1.9×10²¹ FLOP

Published

Jun 25, 2024

JEST++ is an AI model developed by Google DeepMind (United States), first published in June 2024. It works in the vision domain, on tasks such as image classification.

Training it took an estimated 1.9×10²¹ FLOP of compute (estimation method: other). It was trained on roughly 131.1T datapoints. Training ran on 256 Google TPU v5e.

Access: Unreleased. Its weights are not openly released. The reference paper has 30 citations. Epoch AI rates the confidence of this record as confident.

Full record

Organization: Google DeepMind
Country of organization: United States
Domain: Vision
Task: Image classification
Training compute: 1.9×10²¹ FLOP
Compute estimation method: Other
Dataset size: 131.1T
Training hardware: Google TPU v5e
Chips used: 256
Training power draw: 113.7 kW
Model accessibility: Unreleased
Open weights: No
Citations: 30
Epoch confidence: Confident