AI models
Evo
Training compute
2×10²² FLOP
Parameters
7B
Published
Feb 27, 2024
Evo is an AI model developed by Stanford University, University of California (UC) Berkeley and Together (United States), first published in February 2024. It works in the biology domain, on tasks such as protein or nucleotide language model (plm/nlm).
Training it took an estimated 2×10²² FLOP of compute (estimation method: reported). The model has 7,000,000,000 parameters. It was trained on roughly 300B datapoints. Training ran on NVIDIA A100,NVIDIA H100 SXM5 80GB for about 3K hours.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 224 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Stanford University, University of California (UC) Berkeley, Together
- Country of organization
- United States
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
- Training compute
- 2×10²² FLOP
- Compute estimation method
- Reported
- Parameters
- 7,000,000,000
- Dataset size
- 300B
- Training hardware
- NVIDIA A100, NVIDIA H100 SXM5 80GB
- Training time
- 2,968 h
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 224
- 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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