AI models
ProGen2-xlarge
Training compute
1.4×10²² FLOP
Parameters
6.4B
Published
Jun 27, 2022
ProGen2-xlarge is an AI model developed by Salesforce Research, Columbia University and Johns Hopkins University (United States), first published in June 2022. It works in the biology domain, on tasks such as proteins, protein generation and protein or nucleotide language model (plm/nlm).
Training it took an estimated 1.4×10²² FLOP of compute (estimation method: hardware,third-party estimation). The model has 6,400,000,000 parameters. It was trained on roughly 350B datapoints. Training ran on Google TPU v3. The compute alone is estimated at $12K in 2023 dollars.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 507 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Salesforce Research, Columbia University, Johns Hopkins University
- Country of organization
- United States
- Domain
- Biology
- Task
- Proteins, Protein generation, Protein or nucleotide language model (pLM/nLM)
- Training compute
- 1.4×10²² FLOP
- Compute estimation method
- Hardware, Third-party estimation
- Parameters
- 6,400,000,000
- Dataset size
- 350B
- Training hardware
- Google TPU v3
- Training cost (2023 USD)
- $12K
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 507
- Epoch confidence
- Confident
More from Salesforce Research,Columbia University,Johns Hopkins University
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.
← All ai models