AI models
Noisy Student (L2)
Training compute
2.6×10²² FLOP
Parameters
480M
Published
Nov 11, 2019
Noisy Student (L2) is an AI model developed by Carnegie Mellon University (CMU) and Google (United States), first published in November 2019. It works in the vision domain, on tasks such as image classification. It counts among the frontier models: the systems trained with the most compute of their moment.
Training it took an estimated 2.6×10²² FLOP of compute (estimation method: hardware). The model has 480,000,000 parameters. It was trained on roughly 81M datapoints. Training ran on 1,024 Google TPU v3 for about 144 hours. The compute alone is estimated at $46K in 2023 dollars.
Access: Unreleased. Its weights are not openly released. The reference paper has 2,743 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Carnegie Mellon University (CMU), Google
- Country of organization
- United States
- Domain
- Vision
- Task
- Image classification
- Training compute
- 2.6×10²² FLOP
- Compute estimation method
- Hardware
- Parameters
- 480,000,000
- Dataset size
- 81M
- Training hardware
- Google TPU v3
- Chips used
- 1,024
- Training time
- 144 h
- Training power draw
- 944.3 kW
- Training cost (2023 USD)
- $46K
- Numerical format
- FP32
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 2,743
- 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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