AI models
ESM1b
Training compute
5.1×10²¹ FLOP
Parameters
652.4M
Published
Dec 15, 2020
ESM1b is an AI model developed by Facebook AI Research and New York University (NYU) (United States and France), first published in December 2020. It works in the biology domain, on tasks such as proteins and protein or nucleotide language model (plm/nlm).
Training it took an estimated 5.1×10²¹ FLOP of compute (estimation method: hardware,operation counting). The model has 652,400,000 parameters. It was trained on roughly 27.8B datapoints. Training ran on 128 NVIDIA V100. The compute alone is estimated at $924 in 2023 dollars.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 2,637 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Facebook AI Research, New York University (NYU)
- Country of organization
- United States, France
- Domain
- Biology
- Task
- Proteins, Protein or nucleotide language model (pLM/nLM)
- Training compute
- 5.1×10²¹ FLOP
- Compute estimation method
- Hardware, Operation counting
- Parameters
- 652,400,000
- Dataset size
- 27.8B
- Training hardware
- NVIDIA V100
- Chips used
- 128
- Training power draw
- 78.0 kW
- Training cost (2023 USD)
- $924
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 2,637
- Epoch confidence
- Confident
More from Facebook AI Research,New York University (NYU)
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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