AI models
ProxylessNAS
Training compute
3.7×10¹⁸ FLOP
Published
Feb 23, 2019
ProxylessNAS is an AI model developed by Massachusetts Institute of Technology (MIT) (United States), first published in February 2019. It works in the vision domain, on tasks such as image classification and neural architecture search - nas.
Training it took an estimated 3.7×10¹⁸ FLOP of compute (estimation method: hardware). It was trained on roughly 1.2M datapoints. Training ran on NVIDIA V100. The compute alone is estimated at $123 in 2023 dollars.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 2,054 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Massachusetts Institute of Technology (MIT)
- Country of organization
- United States
- Domain
- Vision
- Task
- Image classification, Neural Architecture Search - NAS
- Training compute
- 3.7×10¹⁸ FLOP
- Compute estimation method
- Hardware
- Dataset size
- 1.2M
- Training hardware
- NVIDIA V100
- Chip-hours
- 200
- Training cost (2023 USD)
- $123
- Numerical format
- FP32
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 2,054
- Epoch confidence
- Confident
More from Massachusetts Institute of Technology (MIT)
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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