AI models
Monarch-GPT-2-Small
Training compute
9×10¹⁹ FLOP
Parameters
72M
Published
Apr 1, 2022
Monarch-GPT-2-Small is an AI model developed by Stanford University, University at Buffalo and University of Michigan (United States), first published in April 2022. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 9×10¹⁹ FLOP of compute (estimation method: operation counting). The model has 72,000,000 parameters. It was trained on roughly 103M datapoints. Training ran on NVIDIA V100.
Access: Unreleased. Its weights are not openly released. The reference paper has 130 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Stanford University, University at Buffalo, University of Michigan
- Country of organization
- United States
- Domain
- Language
- Task
- Language modeling
- Training compute
- 9×10¹⁹ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 72,000,000
- Dataset size
- 103M
- Training hardware
- NVIDIA V100
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 130
- Epoch confidence
- Confident
More from Stanford University,University at Buffalo,University of Michigan
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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