AI models
ESM2-3B
Training compute
3×10²² FLOP
Parameters
3B
Published
Jul 21, 2022
ESM2-3B is an AI model developed by Meta AI, New York University (NYU), Stanford University and Massachusetts Institute of Technology (MIT) (United States), first published in July 2022. It works in the biology domain, on tasks such as proteins, protein or nucleotide language model (plm/nlm) and protein folding prediction.
Training it took an estimated 3×10²² FLOP of compute (estimation method: hardware,third-party estimation). The model has 3,000,000,000 parameters. It was trained on roughly 15.4B datapoints.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 636 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Meta AI, New York University (NYU), Stanford University, Massachusetts Institute of Technology (MIT)
- Country of organization
- United States
- Domain
- Biology
- Task
- Proteins, Protein or nucleotide language model (pLM/nLM), Protein folding prediction
- Training compute
- 3×10²² FLOP
- Compute estimation method
- Hardware, Third-party estimation
- Parameters
- 3,000,000,000
- Dataset size
- 15.4B
- Training time
- 720 h
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 636
- Epoch confidence
- Confident
More from Meta AI,New York University (NYU),Stanford University,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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