AI models
Large regularized LSTM
Training compute
4.3×10¹⁶ FLOP
Parameters
66M
Published
Sep 8, 2014
Large regularized LSTM is an AI model developed by New York University (NYU) and Google Brain (United States), first published in September 2014. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 4.3×10¹⁶ FLOP of compute (estimation method: hardware,operation counting). The model has 66,000,000 parameters. It was trained on roughly 929K datapoints. Training ran on 1 NVIDIA Tesla K20c for about 24 hours.
Access: Unreleased. Its weights are not openly released. The reference paper has 3,224 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- New York University (NYU), Google Brain
- Country of organization
- United States
- Domain
- Language
- Task
- Language modeling
- Training compute
- 4.3×10¹⁶ FLOP
- Compute estimation method
- Hardware, Operation counting
- Parameters
- 66,000,000
- Dataset size
- 929K
- Training hardware
- NVIDIA Tesla K20c
- Chips used
- 1
- Training time
- 24 h
- Training power draw
- 264 W
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 3,224
- 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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