AI models
ESMFlow
Training compute
7.6×10²⁰ FLOP
Published
Sep 2, 2024
ESMFlow is an AI model developed by Massachusetts Institute of Technology (MIT) (United States), first published in September 2024. It works in the biology domain, on tasks such as protein folding prediction.
Training it took an estimated 7.6×10²⁰ FLOP of compute (estimation method: reported). It was trained on roughly 553M datapoints. Training ran on 8 NVIDIA A100.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 237 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Massachusetts Institute of Technology (MIT)
- Country of organization
- United States
- Domain
- Biology
- Task
- Protein folding prediction
- Training compute
- 7.6×10²⁰ FLOP
- Compute estimation method
- Reported
- Dataset size
- 553M
- Training hardware
- NVIDIA A100
- Chips used
- 8
- Training power draw
- 6.3 kW
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 237
- Epoch confidence
- Confident
More from Massachusetts Institute of Technology (MIT)
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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