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Graphics processing unit

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  1. Graphics processing unit
Graphics processing unit
Specialized electronic circuit that accelerates graphics

A graphics processing unit (GPU) is a specialized electronic circuit designed for digital image processing and to accelerate computer graphics, being present either as a component on a discrete graphics card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles. GPUs are also increasingly being used for artificial intelligence (AI) processing and model training due to linear algebra acceleration, which is also used extensively in graphics processing.

"GPU" redirects here. For other uses, see GPU (disambiguation).

For an expansion card that contains a graphics processing unit, see Graphics card.

Although there is no single definition of the term, and it may be used to describe any video display system, in modern use a GPU includes the ability to internally perform the calculations needed for various graphics tasks, like rotating and scaling 3D images, and often the additional ability to run custom programs known as shaders. This contrasts with earlier graphics controllers known as video display controllers which had no internal calculation capabilities, or blitters, which performed only basic memory movement operations. The modern GPU emerged during the 1990s, adding the ability to perform operations like drawing lines and text without CPU help, and later adding 3D functionality.

Graphics functions are generally independent and this lends these tasks to being implemented on separate calculation engines. Modern GPUs include hundreds, or thousands, of calculation units. This made them useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. The ability of GPUs to rapidly perform vast numbers of calculations has led to their adoption in diverse fields including artificial intelligence (AI) where they excel at handling data-intensive and computationally demanding tasks. Other non-graphical uses include the training of neural networks and cryptocurrency mining.

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General
  • Recent changes
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This article
Reading time
27 min
Topic
Computer Science
Quality
B-class
Views
14k in 14 days
Last edited
4 Nov 2026
Print/export
  • Printable version
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Last edited
4 Nov 2026