AI models
NAS+ESS (156M)
Training compute
2.9×10¹⁸ FLOP
Parameters
156M
Published
May 6, 2020
NAS+ESS (156M) is an AI model developed by Northeastern University (China), Chinese Academy of Sciences, NiuTrans Research and Kingsoft (China), first published in May 2020. It works in the language domain, on tasks such as neural architecture search - nas and language modeling.
Training it took an estimated 2.9×10¹⁸ FLOP of compute (estimation method: operation counting). The model has 156,000,000 parameters. It was trained on roughly 103M datapoints. Training ran on 1 NVIDIA GeForce GTX 1080 Ti.
Access: Unreleased. Its weights are not openly released. The reference paper has 12 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Northeastern University (China), Chinese Academy of Sciences, NiuTrans Research, Kingsoft
- Country of organization
- China
- Domain
- Language
- Task
- Neural Architecture Search - NAS, Language modeling
- Training compute
- 2.9×10¹⁸ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 156,000,000
- Dataset size
- 103M
- Training hardware
- NVIDIA GeForce GTX 1080 Ti
- Chips used
- 1
- Training power draw
- 280 W
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 12
- 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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