{"id": 1108583, "name": "Computational performance per dollar", "unit": "FLOP/s/$", "createdAt": "2025-10-14T11:42:43.000Z", "updatedAt": "2026-03-23T13:42:22.000Z", "coverage": "", "timespan": "", "datasetId": 7234, "columnOrder": 0, "shortName": "comp_performance_per_dollar", "catalogPath": "grapher/artificial_intelligence/2025-10-10/epoch_gpus/epoch_gpus#comp_performance_per_dollar", "descriptionShort": "Hardware computational performance shown in [floating-point operations](#dod:flop) per second (FLOP/s) per US dollar, adjusted for inflation.", "descriptionProcessing": "- 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).\n- It is not obvious how to adjust this time series for inflation, and we debated it at some length within our team.\n- 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.\n- 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).\n- 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.", "type": "int", "grapherConfigIdETL": "0199e287-a105-7d33-bc58-f2949ad90e99", "dataChecksum": "9984803720683998994", "metadataChecksum": "491522977414985461", "datasetName": "Machine Learning Hardware", "updatePeriodDays": 365, "datasetVersion": "2025-10-10", "nonRedistributable": false, "display": {"unit": "FLOP/s/$", "zeroDay": "2000-01-01", "yearIsDay": true, "numDecimalPlaces": 0}, "schemaVersion": 2, "processingLevel": "major", "presentation": {"topicTagsLinks": ["Artificial Intelligence"]}, "descriptionKey": ["This measures computing power per dollar\u2014specifically, how many calculations per second you get for each inflation-adjusted dollar when buying a GPU.", "GPUs are specialized chips that can perform many calculations simultaneously, making them the primary hardware for training AI systems. The data includes only GPUs used to train major AI models (those with over 1 billion parameters) or specifically designed for machine learning.", "The chart shows theoretical peak performance using a standard precision format (32-bit precision). Modern AI training typically uses lower precision calculations that are faster, so real-world performance may be higher than shown here.", "These figures reflect purchase prices only (adjusted to 2024 dollars). Running costs\u2014electricity, cooling, and infrastructure\u2014are not included here.", "Raw hardware improvements tell only part of the story. Software and algorithmic advances often deliver substantial speedups, independent of better hardware."], "dimensions": {"years": {"values": [{"id": 3089}, {"id": 3965}, {"id": 4699}, {"id": 4798}, {"id": 4808}, {"id": 4891}, {"id": 4952}, {"id": 5074}, {"id": 5162}, {"id": 5375}, {"id": 5434}, {"id": 5554}, {"id": 5792}, {"id": 5939}, {"id": 5991}, {"id": 6100}, {"id": 6118}, {"id": 6246}, {"id": 6247}, {"id": 6278}, {"id": 6305}, {"id": 6381}, {"id": 6391}, {"id": 6544}, {"id": 6660}, {"id": 6799}, {"id": 6837}, {"id": 6891}, {"id": 7439}, {"id": 7549}, {"id": 7583}, {"id": 7772}, {"id": 7982}, {"id": 8111}, {"id": 8123}, {"id": 8298}, {"id": 8342}, {"id": 8372}, {"id": 8659}, {"id": 8713}, {"id": 8740}]}, "entities": {"values": [{"id": 309878, "name": "NVIDIA GeForce GTX 280", "code": null}, {"id": 309821, "name": "NVIDIA GeForce GTX 580", "code": null}, {"id": 309908, "name": "NVIDIA Tesla K20X", "code": null}, {"id": 309986, "name": "NVIDIA Tesla K20c", "code": null}, {"id": 309962, "name": "NVIDIA GeForce GTX TITAN", "code": null}, {"id": 310029, "name": "NVIDIA Quadro K4000", "code": null}, {"id": 309895, "name": "NVIDIA GeForce GTX 780", "code": null}, {"id": 309844, "name": "NVIDIA Quadro K6000", "code": null}, {"id": 370015, "name": "NVIDIA Tesla K40s", "code": null}, {"id": 372226, "name": "NVIDIA Tesla K40t", "code": null}, {"id": 372618, "name": "NVIDIA GeForce GTX Titan Black", "code": null}, {"id": 309910, "name": "NVIDIA GeForce GTX 980", "code": null}, {"id": 370020, "name": "NVIDIA Tesla K80", "code": null}, {"id": 309845, "name": "NVIDIA GeForce GTX TITAN X", "code": null}, {"id": 370023, "name": "NVIDIA M40", "code": null}, {"id": 370018, "name": "NVIDIA P100", "code": null}, {"id": 309693, "name": "NVIDIA GeForce GTX 1080", "code": null}, {"id": 370034, "name": "NVIDIA P40", "code": null}, {"id": 309694, "name": "NVIDIA Quadro P5000", "code": null}, {"id": 309712, "name": "NVIDIA Quadro P6000", "code": null}, {"id": 309783, "name": "NVIDIA Quadro P4000", "code": null}, {"id": 372176, "name": "NVIDIA Quadro P600", "code": null}, {"id": 309705, "name": "NVIDIA GeForce GTX 1080 Ti", "code": null}, {"id": 309708, "name": "NVIDIA TITAN Xp", "code": null}, {"id": 372180, "name": "NVIDIA Tesla V100 PCIe 16 GB", "code": null}, {"id": 372220, "name": "NVIDIA Tesla V100 SXM2", "code": null}, {"id": 370025, "name": "NVIDIA Titan V", "code": null}, {"id": 372221, "name": "NVIDIA Tesla V100 PCIe 32 GB", "code": null}, {"id": 372222, "name": "NVIDIA Tesla V100 SXM2 32 GB", "code": null}, {"id": 309724, "name": "NVIDIA Quadro RTX 5000", "code": null}, {"id": 309748, "name": "NVIDIA Quadro RTX 6000", "code": null}, {"id": 372223, "name": "NVIDIA GeForce RTX 2080 Ti 11GB", "code": null}, {"id": 309695, "name": "NVIDIA Quadro RTX 4000", "code": null}, {"id": 372240, "name": "NVIDIA A100 SXM4 40 GB", "code": null}, {"id": 309738, "name": "NVIDIA GeForce RTX 3080", "code": null}, {"id": 309752, "name": "NVIDIA GeForce RTX 3090", "code": null}, {"id": 309754, "name": "NVIDIA RTX A6000", "code": null}, {"id": 372210, "name": "NVIDIA A10 PCIe", "code": null}, {"id": 370037, "name": "NVIDIA RTX A4000", "code": null}, {"id": 370029, "name": "NVIDIA RTX A5000", "code": null}, {"id": 370044, "name": "AMD Radeon Instinct MI250X", "code": null}, {"id": 372209, "name": "Huawei Ascend 910B", "code": null}, {"id": 370041, "name": "NVIDIA GeForce RTX 3090 Ti", "code": null}, {"id": 370038, "name": "NVIDIA GeForce RTX 4080", "code": null}, {"id": 370014, "name": "NVIDIA GeForce RTX 4090", "code": null}, {"id": 372177, "name": "NVIDIA H100 SXM5 80GB", "code": null}, {"id": 370027, "name": "AMD Radeon RX 7900 XTX", "code": null}, {"id": 372181, "name": "NVIDIA RTX 6000 Ada Generation", "code": null}, {"id": 372203, "name": "NVIDIA L20 PCle", "code": null}, {"id": 372196, "name": "NVIDIA HGX H20", "code": null}, {"id": 372186, "name": "AMD Instinct MI300X", "code": null}]}}, "origins": [{"id": 14153, "title": "Machine Learning Hardware", "description": "This dataset contains detailed information about machine learning hardware, including GPUs, NPUs, and other specialized AI chips. It includes technical specifications such as computational performance across different precision levels (FP64, FP32, FP16, INT8, etc.), memory configurations, release dates, pricing, and manufacturing details.", "producer": "Epoch AI", "citationFull": "Epoch AI, 'Data on Machine Learning Hardware'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/data/machine-learning-hardware' [online resource].", "attributionShort": "Epoch AI", "urlMain": "https://epoch.ai/data/machine-learning-hardware", "urlDownload": "https://epoch.ai/data/ml_hardware.zip", "dateAccessed": "2026-02-27", "datePublished": "2025-10-10", "license": {"url": "https://epoch.ai/data/machine-learning-hardware", "name": "CC BY 4.0"}}, {"id": 14227, "title": "US consumer prices", "description": "The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year.", "producer": "U.S. Bureau of Labor Statistics", "citationFull": "U.S. Bureau of Labor Statistics", "urlMain": "https://www.bls.gov/data/tools.htm", "dateAccessed": "2026-03-20", "datePublished": "2026", "license": {"url": "https://www.bls.gov/opub/copyright-information.htm", "name": "Public domain"}}]}