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Epoch's compute database
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Epoch AI
Epoch AI's compute database is a widely cited empirical resource for understanding AI scaling trends; used frequently in governance and forecasting discussions to ground claims about AI progress trajectories.
Metadata
Importance: 62/100datasettool
Summary
An interactive visualization tool from Epoch AI displaying their database of AI training compute, tracking how computational resources used in notable ML models have evolved over time. It provides empirical data on training compute trends, enabling researchers and forecasters to analyze the scaling of AI systems.
Key Points
- •Tracks training compute (measured in FLOPs) across hundreds of notable AI models over time
- •Enables visual analysis of compute scaling trends, doubling times, and shifts in hardware usage
- •Supports AI forecasting and governance work by grounding predictions in empirical compute data
- •Data spans decades of ML history, illustrating the dramatic acceleration in compute investment
- •Useful for identifying inflection points in AI development trajectories
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Epoch AI | Organization | 51.0 |
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Updated Mar. 19, 2026
# AI Models
Our comprehensive database of over 3200 models tracks key factors driving machine learning progress.
[Download this data](https://epoch.ai/data/ai_models.zip)
Notable AI modelsFrontier AI modelsLarge-scale AI modelsAll AI models
Graph
Table
**Settings**
## Graph
Y Axis
Training compute (FLOP)
Training compute (FLOP)ParametersTraining dataset size (total)Training compute cost (2023 USD)Training time (days)Training power draw (W)
- Training compute (FLOP)
- Parameters
- Training dataset size (total)
- Training compute cost (2023 USD)
- Training time (days)
- Training power draw (W)
X Axis
Publication date
Publication dateTraining compute (FLOP)ParametersTraining dataset size (total)Training compute cost (2023 USD)Training time (days)Training power draw (W)
- Publication date
- Training compute (FLOP)
- Parameters
- Training dataset size (total)
- Training compute cost (2023 USD)
- Training time (days)
- Training power draw (W)
Color by
None
Domain
Organization
Country
Accessibility
## Display
Show Deep Learning Era
Show regressions
Show regresions as
Nx/year
OOM/year
Doubling time
Confidence interval width
%
## Filter
Filter by text
Show only N models, where N is
Apply
### More about this dataset
Documentation
Downloads
Citations
FAQ
### Documentation
Models in this dataset have been collected from various sources, including literature reviews, Papers With Code, historical accounts, highly-cited publications, proceedings of top conferences, and suggestions from individuals. The list of models is non-exhaustive, but aims to cover most models that were state-of-the-art when released, have over 1000 citations, one million monthly active users, or an equivalent level of historical significance. Additional information about our approach to measuring parameter counts, dataset size, and training compute can be found in the accompanying documentation.
[Read the complete documentation](https://epoch.ai/data/ai-models-documentation)
### Frequently asked questions
## What is a notable model?
A notable model meets any of the following criteria: (i) state-of-the-art improvement on a recognized benchmark; (ii) highly cited (over 1000 citations); (iii) historical relevance; (iv) significant use.
## How was the AI Models dataset created?
The dataset was originally created for the report [“Compute Trends Across Three Eras of Machine Learning”](https://epoch.ai/blog/compute-trends) and has continually grown and expanded since then.
## What are notable, frontier, and large-scale models?
We flag models as notable if they advanced the state of the art, achieved many citations in an academic publication, had over a million monthly users, were highly significant historically, or were developed at a cost of over one million dollars. You can learn more about these notability criteria by reading our [AI Models Documentation](ht
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