AI models
Brain2Qwerty
Training compute
1.6×10¹⁸ FLOP
Parameters
400M
Published
Feb 18, 2025
Brain2Qwerty is an AI model developed by Meta AI, Universite de Technologie de Compiègne – CNRS and Basque Center on Cognition (United States, France and Spain), first published in February 2025. It works in the language domain, on tasks such as language generation.
Training it took an estimated 1.6×10¹⁸ FLOP of compute (estimation method: hardware). The model has 400,000,000 parameters. Training ran on 1 NVIDIA V100 for about 12 hours.
Access: Unreleased. Its weights are not openly released. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Meta AI, Universite de Technologie de Compiègne – CNRS, Basque Center on Cognition
- Country of organization
- United States, France, Spain
- Domain
- Language
- Task
- Language generation
- Training compute
- 1.6×10¹⁸ FLOP
- Compute estimation method
- Hardware
- Parameters
- 400,000,000
- Training hardware
- NVIDIA V100
- Chips used
- 1
- Training time
- 12 h
- Training power draw
- 324 W
- Model accessibility
- Unreleased
- Open weights
- No
- 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.
← All ai models