AI models
ConSERT
Training compute
2.8×10²⁰ FLOP
Parameters
340M
Published
May 25, 2021
ConSERT is an AI model developed by Meituan University and Beijing University of Posts and Telecommunications (China), first published in May 2021. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 2.8×10²⁰ FLOP of compute (estimation method: hardware). The model has 340,000,000 parameters. Training ran on NVIDIA Tesla V100S PCIe 32 GB for about 0.1 hours. The compute alone is estimated at $957 in 2023 dollars.
Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 633 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Meituan University, Beijing University of Posts and Telecommunications
- Country of organization
- China
- Domain
- Language
- Task
- Language modeling
- Training compute
- 2.8×10²⁰ FLOP
- Compute estimation method
- Hardware
- Parameters
- 340,000,000
- Training hardware
- NVIDIA Tesla V100S PCIe 32 GB
- Training time
- 0 h
- Training cost (2023 USD)
- $957
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Citations
- 633
- 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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