AI models
LBSTER
Training compute
1.1×10¹⁹ FLOP
Parameters
67M
Published
May 15, 2024
LBSTER is an AI model developed by Prescient Design and Genentech (United States), first published in May 2024. It works in the biology domain, on tasks such as protein or nucleotide language model (plm/nlm).
Training it took an estimated 1.1×10¹⁹ FLOP of compute (estimation method: hardware). The model has 67,000,000 parameters. It was trained on roughly 3.4B datapoints. Training ran on 1 NVIDIA A100 SXM4 80 GB for about 24 hours.
The reference paper has 4 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Prescient Design, Genentech
- Country of organization
- United States
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
- Training compute
- 1.1×10¹⁹ FLOP
- Compute estimation method
- Hardware
- Parameters
- 67,000,000
- Dataset size
- 3.4B
- Training hardware
- NVIDIA A100 SXM4 80 GB
- Chips used
- 1
- Training time
- 24 h
- Training power draw
- 434 W
- Citations
- 4
- 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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