AI models
RoseTTAFold All-Atom (RFAA)
Training compute
2.1×10²⁰ FLOP
Published
Oct 9, 2023
RoseTTAFold All-Atom (RFAA) is an AI model developed by University of Washington, Seoul National University and University of Sheffield (United States, South Korea and United Kingdom), first published in October 2023. It works in the biology domain, on tasks such as protein folding prediction and proteins.
Training it took an estimated 2.1×10²⁰ FLOP of compute (estimation method: hardware). It was trained on roughly 63.2M datapoints. Training ran on NVIDIA RTX A6000.
Access: Open weights (unrestricted). Its weights are openly available. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of Washington, Seoul National University, University of Sheffield
- Country of organization
- United States, South Korea, United Kingdom
- Domain
- Biology
- Task
- Protein folding prediction, Proteins
- Training compute
- 2.1×10²⁰ FLOP
- Compute estimation method
- Hardware
- Dataset size
- 63.2M
- Training hardware
- NVIDIA RTX A6000
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- 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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