Data insights
Epoch AI’s data insights break down complex AI trends into focused, digestible snapshots. Explore topics like training compute, hardware advancements, and AI training costs in a clear and accessible format.
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Biology AI models are scaling 2-4x per year after rapid growth from 2019-2021
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The stock of computing power from NVIDIA chips is doubling every 10 months
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Over 20 AI models have been trained at the scale of GPT-4
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Chinese language models have scaled up more slowly than their global counterparts
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Frontier open models may surpass 1026 FLOP of training compute before 2026
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Training compute growth is driven by larger clusters, longer training, and better hardware
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US models currently outperform non-US models
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Models with downloadable weights currently lag behind the top-performing models
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Accuracy increases with estimated training compute
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AI training cluster sizes increased by more than 20x since 2016
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Performance per dollar improves around 30% each year
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The computational performance of machine learning hardware has doubled every 1.9 years
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The NVIDIA A100 has been the most popular hardware for training notable machine learning models
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Performance improves 12x when switching from FP32 to tensor-INT8
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Leading ML hardware becomes 40% more energy-efficient each year
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Leading AI companies have hundreds of thousands of cutting-edge AI chips
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The power required to train frontier AI models is doubling annually
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The length of time spent training notable models is growing
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Language models compose the large majority of large-scale AI models
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Most large-scale models are developed by US companies
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The pace of large-scale model releases is accelerating
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Almost half of large-scale models have published, downloadable weights
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The size of datasets used to train language models doubles approximately every eight months
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Training compute costs are doubling every nine months for the largest AI models
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The training compute of notable AI models is doubling roughly every five months
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Training compute has scaled up faster for language than vision