AI models
DeLighT
Training compute
3.8×10¹⁸ FLOP
Parameters
99M
Published
Aug 3, 2020
DeLighT is an AI model developed by University of Washington, Allen Institute for AI and Facebook AI Research (United States and France), first published in August 2020. It works in the language domain, on tasks such as language modeling and translation.
Training it took an estimated 3.8×10¹⁸ FLOP of compute (estimation method: operation counting,hardware). The model has 99,000,000 parameters. It was trained on roughly 103M datapoints. Training ran on 8 NVIDIA V100 for about 30 hours.
Access: Unreleased. Its weights are not openly released. The reference paper has 98 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- University of Washington, Allen Institute for AI, Facebook AI Research
- Country of organization
- United States, France
- Domain
- Language
- Task
- Language modeling, Translation
- Training compute
- 3.8×10¹⁸ FLOP
- Compute estimation method
- Operation counting, Hardware
- Parameters
- 99,000,000
- Dataset size
- 103M
- Training hardware
- NVIDIA V100
- Chips used
- 8
- Training time
- 30 h
- Training power draw
- 4.9 kW
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 98
- Epoch confidence
- Likely
More from University of Washington,Allen Institute for AI,Facebook AI Research
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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