AI models
Once for All
Training compute
6.2×10²⁰ FLOP
Parameters
7.7M
Published
Apr 29, 2020
Once for All is an AI model developed by MIT-IBM Watson AI Lab, Massachusetts Institute of Technology (MIT) and IBM (United States), first published in April 2020. It works in the vision domain, on tasks such as image classification.
Training it took an estimated 6.2×10²⁰ FLOP of compute (estimation method: hardware). The model has 7,700,000 parameters. It was trained on roughly 1.3M datapoints. Training ran on NVIDIA V100. The compute alone is estimated at $2K in 2023 dollars.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 1,536 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- MIT-IBM Watson AI Lab, Massachusetts Institute of Technology (MIT), IBM
- Country of organization
- United States
- Domain
- Vision
- Task
- Image classification
- Training compute
- 6.2×10²⁰ FLOP
- Compute estimation method
- Hardware
- Parameters
- 7,700,000
- Dataset size
- 1.3M
- Training hardware
- NVIDIA V100
- Chip-hours
- 4.2K
- Training cost (2023 USD)
- $2K
- Numerical format
- FP32
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 1,536
- 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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