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[Article](https://epoch.ai/blog) [How many AI models will exceed compute thresholds?](https://epoch.ai/blog/model-counts-compute-thresholds)

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# How many AI models will exceed compute thresholds?

We project how many notable AI models will exceed training compute thresholds, with results accessible in an interactive tool. Model counts rapidly increase from 10 above 1e26 FLOP by 2026, to over 200 by 2030.

![](https://epoch.ai/assets/images/posts/2025/model-counts-compute-thresholds/counts-over-time-cumulative-banner.png)

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### Published

May 30, 2025

### Authors

Ben Cottier,
David Owen

### Resources

[![](https://epoch.ai/assets/images/icons/sliders.svg)\\
Simulator](https://epoch.ai/tools/model-counts)

## Executive summary

The compute used to train AI models has been a key driver of AI progress, informing many predictions of AI’s future capabilities. However, the _number_ of AI models that will surpass different compute levels has received less attention. This is relevant to compute-based AI regulation, as well as AI development and deployment more broadly. We develop a projective model that relates key inputs such as investment and the distribution of compute to the number of [notable AI models](https://epoch.ai/data/ai-models): models that are state of the art, highly cited, or otherwise historically notable. The projections can be explored in a new [interactive tool](https://epoch.ai/tools/model-counts).

Cumulative number of notable AI models by year

Median projection for different training compute thresholds.

2022202320242025202620272028202920300100200300400500YearCumulative number of models>1024 FLOP>1025 FLOP>1026 FLOP>1027 FLOP

Number of new notable AI models in each year

Median projection for different training compute thresholds.

202220232024202520262027202820292030050100150YearNumber of new models>1024 FLOP>1025 FLOP>1026 FLOP>1027 FLOP

Show

Cumulative number of models

Cumulative number of models

Number of new models

Cumulative number of modelsNumber of new models

Figure 1: Median projection for future notable AI model releases with different levels of compute, by year. Note: these projections are likely to be smaller than total model counts as a compute threshold falls further behind the frontier, since lower-compute models are less likely to meet Epoch AI’s notability criteria or be publicly documented.

[CC-BY](https://creativecommons.or

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