AI models
DRGN-AI
Training compute
6.5×10¹⁹ FLOP
Published
Jun 2, 2024
DRGN-AI is an AI model developed by Stanford University, SLAC National Laboratory, Princeton University and Columbia University (United States), first published in June 2024. It works in the biology domain, on tasks such as cryo-em image reconstruction.
Training it took an estimated 6.5×10¹⁹ FLOP of compute. It was trained on roughly 11.6B datapoints. Training ran on 4 NVIDIA A100 for about 48 hours.
Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Stanford University, SLAC National Laboratory, Princeton University, Columbia University
- Country of organization
- United States
- Domain
- Biology
- Task
- Cryo-EM image reconstruction
- Training compute
- 6.5×10¹⁹ FLOP
- Dataset size
- 11.6B
- Training hardware
- NVIDIA A100
- Chips used
- 4
- Training time
- 48 h
- Training power draw
- 3.2 kW
- Epoch confidence
- Confident
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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