GPU computational performance per dollar

Graphics processing units (GPUs) are the dominant computing hardware for artificial intelligence systems. GPUperformance is shown in floating-point operations operations/second (FLOP/s) per US dollar, adjusted for inflation.

Release dateJun 16, 2008Nov 4, 2011Jul 31, 2014Apr 26, 2017Jan 21, 2020Oct 17, 2022FLOP/s/$ (FLOP/s/$)1 billion10 billionNVIDIA GeForce GTX 280NVIDIA GeForce GTX 280NVIDIA GeForce GTX 580NVIDIA GeForce GTX 580NVIDIA Tesla K20cNVIDIA Tesla K20cNVIDIA GTX Titan BlackNVIDIA GTX Titan BlackNVIDIA M40NVIDIA M40NVIDIA P100NVIDIA P100NVIDIA Quadro P4000NVIDIA Quadro P4000NVIDIA Geforce GTX 1080 TiNVIDIA Geforce GTX 1080 TiNVIDIA Quadro RTX 6000NVIDIA Quadro RTX 6000NVIDIA GeForce RTX 3080NVIDIA GeForce RTX 3080NVIDIA A100NVIDIA A100ManufacturerAMDNVIDIA

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    GPU computational performance per dollar

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    About this data

    GPU computational performance per dollar
    Graphics processing units (GPUs) are the dominant computing hardware for artificial intelligence systems. GPU performance is shown in operations/second (FLOP/s) per US dollar, adjusted for inflation.
    Source
    Epoch (2024) – with major processing by Our World in Data
    Last updated
    July 11, 2024
    Next expected update
    July 2025
    Unit
    FLOP/s/$

    Sources and processing

    This data is based on the following sources

    This dataset contains performance data for 53 GPUs and AI chips used in machine learning experiments from 2008 to 2023. It includes details on computational performance, memory capacities and bandwidths, and interconnect bandwidths.

    Retrieved on
    July 26, 2024
    Citation
    This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
    Epoch AI, 'Data on ML GPUs'. Forthcoming.

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    Notes on our processing step for this indicator
    • Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across time; it is therefore not very useful. To make comparisons across time possible, one has to take into account that prices change (e.g., there is inflation).
    • It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team.
    • It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services that these investments purchased. This would make it possible to calculate a volume measure of AI investments, and it would tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost for some crucial AI technology has fallen rapidly in price.
    • In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. In the end we decided to use the US Consumer Price Index (CPI).
    • The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments therefore lets us understand the size of these investments relative to whatever else these sums of money could have purchased.

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    • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
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    Citations

    How to cite this page

    To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

    “Data Page: GPU computational performance per dollar”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch. Retrieved from https://ourworldindata.org/grapher/gpu-price-performance [online resource]
    How to cite this data

    In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

    Epoch (2024) – with major processing by Our World in Data

    Full citation

    Epoch (2024) – with major processing by Our World in Data. “GPU computational performance per dollar” [dataset]. Epoch, “Trends in Machine Learning Hardware” [original data]. Retrieved April 18, 2025 from https://ourworldindata.org/grapher/gpu-price-performance