What is GPU?

Published: 26 February 2024
Contributors: Mesh Flinders, Ian Smalley

What is a graphics processing unit (GPU)?

A graphics processing unit, also known as a graphical processing unit or GPU, is an electronic circuit designed to speed computer graphics and image processing on a variety of devices, including video cards, motherboards, mobile phones and personal computers (PCs).

By performing mathematical calculations rapidly, a GPU reduces the amount of time needed for a computer to run multiple programs, making it an essential enabler of emerging and future technologies like machine learning (ML), artificial intelligence (AI) and blockchain.

Before the invention of GPUs in the 1990s, graphics controllers in PCs and on video game controllers relied on a computer’s central processing unit (CPU), to execute tasks. Since the early 1950s, CPUs were the most important processors in a computer, executing all instructions necessary to run programs, such as logic, controlling and input/output (I/O). With the advent of personal gaming and computer-aided design (CAD) in the 1990s however, the industry needed a faster, more efficient way to combine pixels in a short amount of time.

In 2007, Nvidia built CUDA (Compute Unified Device Architecture), software that gave developers direct access to GPUs’ parallel computation abilities, empowering them to use GPU technology for a wider range of functions than before. In the 2010s, GPU technology gained even more capabilities, perhaps most significantly ray tracing (the generation of computer images by tracing the direction of light from a camera) and tensor cores (designed to enable deep learning).

Because of these advancements, GPUs have played important roles in AI acceleration and deep learning processors, helping speed the development of AI and ML applications. Today, in addition to powering gaming consoles and editing software, GPUs power cutting edge compute functions critical to many enterprises.

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What are the different types of GPUs?

There are three types of GPUs, discrete GPUs, integrated GPUs and virtual GPUs:

Discrete: Discrete GPUs, or dGPUs, are graphics processors that are separate from a device’s CPU, where information is taken in and processed, allowing a computer to function. Discrete GPUs are typically used in advanced applications with special requirements, such as video editing, content creation or high-end gaming. They are distinct chips with connectors to separate circuit boards that are usually attached to the CPU using an express slot. One of the most widely used discrete GPUs is the Intel Arc brand, which was built for the PC gaming industry.

Integrated: An integrated GPU, or iGPU, is built-in to a computer or device’s infrastructure and typically slotted in next to the CPU. Designed in the 2010s by Intel, integrated GPUs became more popular as manufacturers such as MSI, ASUS and Nvidia noticed the power of combining GPUs with CPUs rather than requiring users to add GPUs via a PCI express slot themselves. Today, they remain a popular choice for laptop users, gamers and others running compute intensive programs on their PCs.

Virtual: Virtual GPUs have the same capabilities as discrete or integrated GPUs, but without the hardware. They are simply a software-based version of a GPU built for a cloud instance and can be used to run the same workloads. Also, since they have no hardware, they are simpler and cheaper to maintain than their physical counterparts.

Modern GPU use cases

As GPUs developed over time, technical improvements made them more programmable, and more capabilities were discovered. Specifically, their ability to divide tasks across more than one processor—known as parallel processing—has made them indispensable to a wide range of applications, such as PC gaming, high performance computing (HPC), 3D rendering workstations, data center computing and many others. Here’s a closer look at some of the most important, modern applications of GPU technology:

Artificial intelligence

AI and its many applications would arguably be impossible without GPU technology. GPUs’ ability to solve highly technical problems faster and more efficiently than traditional CPUs makes them indispensable. GPUs power many leading AI applications, such as IBM’s cloud-native AI supercomputer Vela, that require high speeds in order to train on larger and larger data sets. AI models train and run on data center GPUs, typically operated by enterprises conducting scientific research or other compute-intensive tasks.

Machine learning (ML) and deep learning (DL)

Machine learning, or ML, refers to a specific discipline of AI concerned with the use of data and algorithms to imitate the way humans learn. Deep learning, or DL, is a subset of ML that uses neural networks to simulate the decision-making process of the human brain. GPU technology is critical to both areas of technological advancement.

When it comes to ML and DL, GPUs power the models’ ability to sort through massive data sets and make inferences from them in a similar way to humans. GPUs specifically enhance the areas of memory and optimization because they can perform many simultaneous calculations at once. Additionally, GPUs used in ML and DL use fewer resources than CPUs without a dip in power or accuracy.

Blockchain

Blockchain, the popular ledger used to record transactions and track assets in business networks, relies heavily on GPU technology, especially when it comes to a step called "proof of work." In many widely used blockchains, such as cryptocurrencies, the proof of work step is vital to the validation of a transaction, allowing it to be added to the blockchain.

Gaming

The gaming industry first tapped the power of GPUs in the 1990s to improve the overall gaming experience with more speed and graphical accuracy. Today, personal gaming is highly compute-intensive because of hyper-real scenarios, real-time interactions and vast, immersive in-game worlds. Trends in gaming like virtual reality (VR), higher refresh rates and higher resolution screens all depend on GPUs to speedily deliver graphics in more and more demanding compute environments.

Video editing

Traditionally, long render times have been a big blocker in both consumer and professional editing software applications. Since their invention, GPUs have steadily reduced processing times and compute resources in popular video editing products like Final Cut Pro and Adobe Premiere. Today, GPUs equipped with parallel processing and built-in AI dramatically speed editing capabilities for everything from professional editing suites to smartphone apps.

Content creation

Improvements in processing, performance and graphics quality have made GPUs an essential part of the transformation of the content-creation industry. Today, content creators equipped with a top-performing graphics card and high-speed internet can generate realistic content, augment it with AI and machine learning, and then edit and stream it to a live audience faster than ever before. All thanks in large part to advancements in GPU technology.

Visualization and simulation

GPUs are in high demand across many industries to enhance the experience and training capabilities of complex, professional applications, including product walkthroughs, CAD drawings and medical and seismic/geophysical imaging. GPUs are critical in advanced visualizations used in the professional training of firefighters, astronauts, schoolteachers and others with 3D animation, AI and ML, advanced rendering and hyper realistic virtual reality (VR) and augmented reality (AR) experiences.

Additionally, engineers and climate scientists use simulation applications powered by GPUs to predict weather conditions, fluid dynamics, astrophysics and how vehicles will behave under certain conditions. One of the most powerful GPUs available for these purposes is the Nvidia geforce RTX chip, built primarily for scientific visualization and energy exploration.

How does a GPU work?

Today’s GPUs utilize many multiprocessors to handle all the different parts of the task they’ve been given.

A GPU has its own rapid access memory (RAM)—a specific kind of electronic memory used to store code and data that the chip can access and alter as needed. Advanced GPUs typically have RAM that has been specifically built to hold the large data volumes required for compute-intensive tasks like graphics editing, gaming or AI/ML use-cases.

Two popular kinds of GPU memory are Graphics Double Data Rate 6 Synchronous Dynamic Random-Access Memory (GDDR6) and GDDR6X, a later generation. GDDR6X consumes 15% less power per transferred bit than GDDR6, but its overall power consumption is higher since GDDR6X is faster. iGPUs can either be integrated into a computer’s CPU or inserted into a slot alongside it and connected via a PCI express port.

What’s the difference between a GPU and a CPU?

CPUs and GPUs share a similar design, including a similar number of cores and transistors for processing tasks, but CPUs are more general-purpose in their functions than GPUs. GPUs tend to be focused on a singular, specific computing task, such as graphics processing or machine learning.

CPUs are the heart and brain of a computer system or device. They receive general instructions or requests regarding a task from a program or software application. A GPU, on the other hand, has a more specific task—typically involving the processing of high-resolution images and videos quickly. To accomplish their task, GPUs constantly perform complex mathematical calculations required for rendering graphics or other compute intensive functions.

One of the biggest differences between CPUs and GPUs is that CPUs tend to use fewer cores and perform their tasks in a linear order, while GPUs have hundreds—even thousands—of cores, enabling the parallel processing that drives their lightning-fast processing capabilities.

The first GPUs were built to speed 3D graphics rendering, making movie and video game scenes seem more realistic and engaging. The first GPU chip, the GeForce from Nvidia, was released in 1999, and was quickly followed by a rapid period of growth that saw GPU capabilities expand into other areas due to their high-speed parallel processing capabilities.

Parallel processing, or parallel computing, is a kind of computing that relies on two or more processors to accomplish different subsets of an overall computing task. Before GPUs, older generation computers could only run one program at a time, often taking hours to complete a task. GPUs' parallel processing function performs many calculations or tasks simultaneously, making them faster and more efficient than CPUs in older computers.