AI models
RNA-FrameFlow
Training compute
3.7×10¹⁸ FLOP
Parameters
16.8M
Published
Jun 19, 2024
RNA-FrameFlow is an AI model developed by National University of Singapore, Prescient Design, University of Missouri and University of Cambridge (Singapore, United States and United Kingdom), first published in June 2024. It works in the biology domain, on tasks such as rna design.
Training it took an estimated 3.7×10¹⁸ FLOP of compute (estimation method: hardware). The model has 16,800,000 parameters. Training ran on 4 NVIDIA GeForce RTX 3090 for about 18 hours.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 11 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- National University of Singapore, Prescient Design, University of Missouri, University of Cambridge
- Country of organization
- Singapore, United States, United Kingdom
- Domain
- Biology
- Task
- RNA design
- Training compute
- 3.7×10¹⁸ FLOP
- Compute estimation method
- Hardware
- Parameters
- 16,800,000
- Training hardware
- NVIDIA GeForce RTX 3090
- Chips used
- 4
- Training time
- 18 h
- Training power draw
- 2.8 kW
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 11
- 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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