AI models
TrellisNet
Training compute
2.8×10¹⁸ FLOP
Parameters
180M
Published
Oct 15, 2018
TrellisNet is an AI model developed by Carnegie Mellon University (CMU), Bosch Center for Artificial Intelligence and Intel Labs (United States and Germany), first published in October 2018. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 2.8×10¹⁸ FLOP of compute (estimation method: operation counting). The model has 180,000,000 parameters. It was trained on roughly 103M datapoints.
Access: Unreleased. Its weights are not openly released. The reference paper has 164 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Carnegie Mellon University (CMU), Bosch Center for Artificial Intelligence, Intel Labs
- Country of organization
- United States, Germany
- Domain
- Language
- Task
- Language modeling
- Training compute
- 2.8×10¹⁸ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 180,000,000
- Dataset size
- 103M
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 164
- 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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