AI models
RFdiffusion
Published
Jul 23, 2023
RFdiffusion is an AI model developed by University of Washington, Columbia University, Ecole Normale Supèrieure, University of Cambridge, Massachusetts Institute of Technology (MIT) and Seoul National University (United States, France, United Kingdom and South Korea), first published in July 2023. It works in the biology domain, on tasks such as protein generation and protein folding prediction.
Epoch AI has no training-compute estimate for this model. Training ran on 64 NVIDIA V100 for about 672 hours.
Access: Open weights (unrestricted). Its weights are openly available. It is built on top of RoseTTAFold All-Atom (RFAA). Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of Washington, Columbia University, Ecole Normale Supèrieure, University of Cambridge, Massachusetts Institute of Technology (MIT), Seoul National University
- Country of organization
- United States, France, United Kingdom, South Korea
- Domain
- Biology
- Task
- Protein generation, Protein folding prediction
- Compute estimation method
- Hardware
- Training hardware
- NVIDIA V100
- Chips used
- 64
- Training time
- 672 h
- Training power draw
- 38.2 kW
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Base model
- RoseTTAFold All-Atom (RFAA)
- 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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