Computation used to train notable artificial intelligence systems

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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
Computation is measured in total petaFLOP, which is 10¹⁵ estimated from AI literature, albeit with some uncertainty.
Epoch (2024) – with major processing by Our World in Data
Last updated
July 10, 2024
Next expected update
August 2024

Sources and processing

This data is based on the following sources

Retrieved on
July 10, 2024
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, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at Retrieved from: ‘’ [online resource]

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Notes on our processing step for this indicator

Training computation was converted from its original measurement in FLOPs (floating-point operations) to a more manageable unit known as petaFLOPs. This conversion is performed by dividing the original training compute value by 1e15, which represents one quadrillion (10^15). The purpose of this conversion is to provide a more human-readable and practical representation of the immense computational efforts involved in training AI systems. By expressing the training computation in petaFLOPs, it becomes easier to grasp the scale and magnitude of the computational resources required for training these systems, especially when dealing with large datasets and complex architectures.

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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: Computation used to train notable artificial intelligence systems”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch. Retrieved from [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

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Epoch (2024) – with major processing by Our World in Data. “Computation used to train notable artificial intelligence systems” [dataset]. Epoch, “Parameter, Compute and Data Trends in Machine Learning” [original data]. Retrieved July 19, 2024 from