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Computation used to train notable AI systems, by affiliation of researchers

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Artificial Intelligence

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 AI systems, by affiliation of researchers
Computation is measured in total petaFLOP, which is 10¹⁵ estimated from AI literature, albeit with some uncertainty.
Source
Epoch (2024) – with major processing by Our World in Data
Last updated
October 1, 2024
Next expected update
December 2024

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Sources and processing

This data is based on the following sources

Retrieved on
October 1, 2024
Retrieved from
https://epochai.org/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.
Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’ [online resource]

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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 AI systems, by affiliation of researchers”, 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/artificial-intelligence-training-computation-by-researcher-affiliation [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. “Computation used to train notable AI systems, by affiliation of researchers” [dataset]. Epoch, “Parameter, Compute and Data Trends in Machine Learning” [original data]. Retrieved November 18, 2024 from https://ourworldindata.org/grapher/artificial-intelligence-training-computation-by-researcher-affiliation