e systems, by domain | Our World in Data Our World in Data Browse by topic Data Latest Resources About Subscribe Donate What you should know about this indicator In the context of artificial intelligence (AI), training computation is predominantly measured using floating-point operations or “FLOP”. One FLOP represents a single arithmetic operation involving floating-point numbers, such as addition, subtraction, multiplication, or division. To adapt to the vast computational demands of AI systems, the measurement unit of petaFLOP is commonly used. One petaFLOP stands as a staggering one quadrillion FLOPs, underscoring the magnitude of computational operations within AI. Modern AI systems are rooted in machine learning and deep learning techniques. These methodologies are notorious for their computational intensity, involving complex mathematical processes and algorithms. During the training phase, AI models process large volumes of data, while continuously adapting and refining their parameters to optimize performance, rendering the training process computationally intensive. Many factors influence the magnitude of training computation within AI systems. Notably, the size of the dataset employed for training significantly impacts the computational load. Larger datasets necessitate more processing power. The complexity of the model's architecture also plays a pivotal role; more intricate models lead to more computations. Parallel processing, involving the simultaneous use of multiple processors, also has a substantial effect. Beyond these factors, specific design choices and other variables further contribute to the complexity and scale of training computation within AI. Computation used to train notable artificial intelligence systems, by domain Computation is measured in total petaFLOP, which is 10¹⁵ floating-point operations estimated from AI literature, albeit with some uncertainty. Source Epoch AI (2026) – with major processing by Our World in Data Last updated March 12, 2025 Next expected update July 2026 Explore charts that include this data Computation used to train notable AI systems, by affiliation of researchers Training computation vs. parameters in notable AI systems, by domain Training computation vs. parameters in notable AI systems, by researcher affiliation Training computation vs. dataset size in notable AI systems, by researcher affiliation Chart 1 of 4 More Data on Artificial Intelligence Sources and processing This data is based on the following sources Epoch AI – Parameter, Compute and Data Trends in Machine Learning Retrieved on June 11, 2026 Retrieved from https://epoch.ai/mlinputs/visualization 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. 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