AI models
ENAS
Training compute
2×10¹⁶ FLOP
Parameters
24M
Published
Feb 9, 2018
ENAS is an AI model developed by Google Brain, Carnegie Mellon University (CMU) and Stanford University (United States), first published in February 2018. It works in the language domain, on tasks such as language modeling and neural architecture search - nas.
Training it took an estimated 2×10¹⁶ FLOP of compute (estimation method: operation counting). The model has 24,000,000 parameters. It was trained on roughly 929K datapoints. Training ran on NVIDIA GeForce GTX 1080 Ti.
Access: Unreleased. Its weights are not openly released. The reference paper has 3,004 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Google Brain, Carnegie Mellon University (CMU), Stanford University
- Country of organization
- United States
- Domain
- Language
- Task
- Language modeling, Neural Architecture Search - NAS
- Training compute
- 2×10¹⁶ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 24,000,000
- Dataset size
- 929K
- Training hardware
- NVIDIA GeForce GTX 1080 Ti
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 3,004
- 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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