{"id": 930598, "name": "Cost (inflation-adjusted)", "unit": "constant 2023 US$", "createdAt": "2024-06-10T11:03:07.000Z", "updatedAt": "2025-09-25T19:29:50.000Z", "coverage": "", "timespan": "24447-27367", "datasetId": 6553, "shortUnit": "$", "columnOrder": 0, "shortName": "cost__inflation_adjusted", "catalogPath": "grapher/artificial_intelligence/2024-06-06/epoch_compute_cost/epoch_compute_cost#cost__inflation_adjusted", "descriptionShort": "The cost of developing the AI system expressed in 2023 US dollars, adjusted for inflation.", "descriptionFromProducer": "The cost of developing the AI system was adjusted for inflation using the Consumer Price Index (CPI) for the United States. The CPI was used to account for the changes in the price level of consumer goods and services over time. The inflation-adjusted cost is presented in 2023 US dollars.", "type": "float", "dataChecksum": "11500422025950000985", "metadataChecksum": "-7081184986063020146", "datasetName": "The rising costs of training frontier AI models", "datasetVersion": "2024-06-06", "nonRedistributable": false, "display": {"name": "Cost", "unit": "constant 2023 US$", "shortUnit": "$", "numDecimalPlaces": 0}, "schemaVersion": 2, "processingLevel": "minor", "presentation": {"topicTagsLinks": ["Artificial Intelligence"]}, "dimensions": {"years": {"values": [{"id": 24837}, {"id": 25127}, {"id": 25869}, {"id": 25175}, {"id": 26974}, {"id": 25472}, {"id": 26884}, {"id": 26445}, {"id": 26302}, {"id": 24447}, {"id": 27276}, {"id": 26955}, {"id": 26878}, {"id": 26644}, {"id": 24740}, {"id": 26505}, {"id": 26080}, {"id": 26994}, {"id": 27101}, {"id": 27367}, {"id": 26639}, {"id": 26550}, {"id": 27353}, {"id": 25027}, {"id": 26703}, {"id": 25959}, {"id": 25826}, {"id": 26581}, {"id": 26357}, {"id": 26842}, {"id": 24859}, {"id": 26784}, {"id": 26756}, {"id": 27157}, {"id": 26835}, {"id": 24792}, {"id": 26421}, {"id": 25748}, {"id": 26308}, {"id": 25862}, {"id": 25975}, {"id": 24751}, {"id": 26100}]}, "entities": {"values": [{"id": 257033, "name": "AlphaGo Master", "code": null}, {"id": 257039, "name": "AlphaGo Zero", "code": null}, {"id": 257060, "name": "AlphaStar", "code": null}, {"id": 240145, "name": "AlphaZero", "code": null}, {"id": 368746, "name": "BLOOM-176B", "code": null}, {"id": 257044, "name": "BigGAN-deep 512x512", "code": null}, {"id": 368716, "name": "BlenderBot 3", "code": null}, {"id": 368326, "name": "ByT5-XXL", "code": null}, {"id": 257077, "name": "DALL-E", "code": null}, {"id": 365389, "name": "DeepSpeech2 (English)", "code": null}, {"id": 369508, "name": "Falcon-180B", "code": null}, {"id": 369171, "name": "Flan-PaLM 540B", "code": null}, {"id": 365992, "name": "GLM-130B", "code": null}, {"id": 368065, "name": "GLaM", "code": null}, {"id": 257030, "name": "GNMT", "code": null}, {"id": 273165, "name": "GOAT", "code": null}, {"id": 354864, "name": "GPT-3 175B (davinci)", "code": null}, {"id": 367638, "name": "GPT-3.5 (text-davinci-003)", "code": null}, {"id": 363052, "name": "GPT-4", "code": null}, {"id": 369506, "name": "Gemini 1.0 Ultra", "code": null}, {"id": 367255, "name": "Gopher (280B)", "code": null}, {"id": 369507, "name": "HyperCLOVA 82B", "code": null}, {"id": 368751, "name": "Inflection-2", "code": null}, {"id": 257037, "name": "JFT", "code": null}, {"id": 257095, "name": "LaMDA", "code": null}, {"id": 257064, "name": "Meena", "code": null}, {"id": 257055, "name": "Megatron-BERT", "code": null}, {"id": 368027, "name": "Megatron-LM (8.3B)", "code": null}, {"id": 257092, "name": "Megatron-Turing NLG 530B", "code": null}, {"id": 257104, "name": "Meta Pseudo Labels", "code": null}, {"id": 306195, "name": "Minerva (540B)", "code": null}, {"id": 257035, "name": "MoE", "code": null}, {"id": 306188, "name": "OPT-175B", "code": null}, {"id": 273167, "name": "PaLM (540B)", "code": null}, {"id": 365387, "name": "PaLM 2", "code": null}, {"id": 306194, "name": "Parti", "code": null}, {"id": 306078, "name": "PolyNet", "code": null}, {"id": 257082, "name": "ProtT5-XXL", "code": null}, {"id": 365388, "name": "RoBERTa Large", "code": null}, {"id": 257078, "name": "Switch", "code": null}, {"id": 257059, "name": "T5-11B", "code": null}, {"id": 368060, "name": "Turing-NLG", "code": null}, {"id": 368739, "name": "U-PaLM (540B)", "code": null}, {"id": 240142, "name": "Xception", "code": null}, {"id": 257070, "name": "iGPT-XL", "code": null}]}}, "origins": [{"id": 8746, "title": "The rising costs of training frontier AI models", "descriptionSnapshot": "This data is specifically from Figure 1 of the paper.", "description": "This dataset provides a comprehensive analysis of the costs associated with training frontier AI models, with a focus on estimating the magnitude and growth of these expenses. It includes detailed cost components for various AI models, considering hardware, energy, cloud rental, and staff expenses. The dataset is based on a detailed cost model developed to fill the gap in public data on AI training costs. The data covers the period from 2016 to the projected costs for 2027.", "producer": "Epoch AI", "citationFull": "Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej, and David Owen. \u2018The rising costs of training frontier AI models\u2019. ArXiv [cs.CY], 2024. arXiv. https://arxiv.org/abs/2405.21015.", "versionProducer": "2024-05-31", "urlMain": "https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models", "dateAccessed": "2024-06-06", "datePublished": "2024-05-31", "license": {"name": "CC BY 4.0"}}]}