AI models
DiscDiff
Training compute
3.4×10¹⁹ FLOP
Published
Feb 8, 2024
DiscDiff is an AI model developed by Imperial College London (United Kingdom), 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 3.4×10¹⁹ FLOP of compute (estimation method: hardware). It was trained on roughly 983M datapoints. Training ran on 2 NVIDIA RTX A6000,NVIDIA A100.
The reference paper has 21 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Imperial College London
- Country of organization
- United Kingdom
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
- Training compute
- 3.4×10¹⁹ FLOP
- Compute estimation method
- Hardware
- Dataset size
- 983M
- Training hardware
- NVIDIA RTX A6000, NVIDIA A100
- Chips used
- 2
- Citations
- 21
- 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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