AI models
AntiFormer
Training compute
1.7×10¹⁸ FLOP
Parameters
24.7M
Published
Aug 20, 2024
AntiFormer is an AI model developed by University of Florida, Sichuan University, Shihezi University, University of Macau and University of Texas Health Science Center (United States, China and Macao), first published in August 2024. It works in the biology domain, on tasks such as protein protein binding affinity prediction.
Training it took an estimated 1.7×10¹⁸ FLOP of compute (estimation method: hardware). The model has 24,670,596 parameters. Training ran on 1 NVIDIA A100 SXM4 40 GB.
Access: Open weights (unrestricted). Its weights are openly available. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of Florida, Sichuan University, Shihezi University, University of Macau, University of Texas Health Science Center
- Country of organization
- United States, China, Macao
- Domain
- Biology
- Task
- Protein protein binding affinity prediction
- Training compute
- 1.7×10¹⁸ FLOP
- Compute estimation method
- Hardware
- Parameters
- 24,670,596
- Training hardware
- NVIDIA A100 SXM4 40 GB
- Chips used
- 1
- Training power draw
- 433 W
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 0
- 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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