AI models
RoBERTa (PFAM)
Training compute
1.2×10¹⁹ FLOP
Published
Dec 5, 2020
RoBERTa (PFAM) is an AI model developed by IBM Research and ETH Zurich (United States and Switzerland), first published in December 2020. It works in the biology domain, on tasks such as protein or nucleotide language model (plm/nlm).
Training it took an estimated 1.2×10¹⁹ FLOP of compute (estimation method: operation counting). Training ran on 4 NVIDIA P100.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 19 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- IBM Research, ETH Zurich
- Country of organization
- United States, Switzerland
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
- Training compute
- 1.2×10¹⁹ FLOP
- Compute estimation method
- Operation counting
- Training hardware
- NVIDIA P100
- Chips used
- 4
- Training power draw
- 2.0 kW
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 19
- Epoch confidence
- Likely
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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