Take a virtual tour inside the NVIDIA GA102 graphics processing unit to explore the architecture that enables it to perform over 35 trillion calculations per second.

With many smaller computing cores, graphics processing units process many identical tasks in parallel, whereas central processing units have fewer, larger cores built to handle a wide variety of more complex tasks sequentially.

The history of graphics processing units began with hardware for displaying visuals in arcade machines, but the transition to 3D graphics led to the microchips and software that power today's AI data centers.

Graphics processing units, which process large amounts of data in parallel, have become the foundational hardware for AI systems that rely on hardware capable of processing millions of text parameters simultaneously.

CUDA, the computing platform that transformed graphics processing units from pixel renderers into general-purpose components powering AI, originated from a computer science student.

Graphic processing units rely on a hierarchy of memory for optimization, where the smallest, fastest memory—registers—is located closest to the core and provides access to data in a fraction of a nanosecond.

Although GPU stands for graphics processing unit, the acronym is often used to refer to a graphics card—an assembly that houses the microchip along with supporting components.

CUDA was Nvidia’s first step into artificial intelligence

CUDA (Compute Unified Device Architecture) is a parallel computing platform that was developed by Nvidia in the mid-2000s. By letting people perform computations in parallel, rather than sequentially, CUDA allowed people to compute large amounts of data quickly. CUDA has been used to build machine-learning models as well.

The term GPU was first used by Sony in 1994 to refer to the geometry processing unit inside the PlayStation console, five years before Nvidia's marketing of the GeForce 256 revised the acronym to graphics processing unit.

Watch a tech demo of the world's first consumer graphics processing unit, the Nvidia GeForce 256, released Oct. 11, 1999.

Nvidia's rapid development of GPUs is forcing computing to depart from something called Moore's law

Moore’s law, named after Intel’s cofounder, predicts that the number of transistors on a chip will double every two years. But Nvidia could be outpacing that prediction.

By 2024, Nvidia had become one of the world’s most valuable companies

Nvidia started as a graphics chip designer for PCs and consoles. This explainer traces its rise from early GPU innovation to powering AI breakthroughs and fueling the data centers behind today’s biggest tech platforms.

Nvidia pioneered the GPU industry via its origins as a video game company

From the mid-1990s to early 2000s, Nvidia carved out a niche as a maker of semiconductor chips specially designed for the video game industry. Nvidia broke through the competition to seal deals with TSMC and Microsoft, and popularized the term "graphics processing unit," aka GPU.

Some graphics processing units advertised under different product names, such as Nvidia's RTX 5070-Ti and RTX 5080, are identically manufactured microchips with different defect counts.

NVIDIA named several computer chip architectures after Grace Hopper

Unveiled in 2023, the GH200 Grace Hopper Superchip, designed for generative AI applications in data center-scale systems, combines a Grace CPU with a Hopper GPU to create a coherent memory model. This allows the Hopper to access Grace data directly, reducing the need to repeatedly copy data back and forth and vastly improving performance.

Explore graphics processing unit benchmarks for 2026, led in performance by Nvidia's GeForce RTX 5090, with AMD's Radeon RX 9060 XT 8GB and Nvidia's GeForce RTX 5060 providing the best 1080p performance per dollar.

As of 2025, Nvidia controls over 90% of the discrete graphics processing unit market, with its high-end components serving as the backbone for the AI data center boom, followed by AMD and Intel.

Nvidia controls more than four-fifths of the GPU market—but isn't considered a monopoly

Semiconductor company Nvidia has a commanding market share when it comes to graphics processing units. Its competitors include Advanced Micro Devices and Intel, which together account for less than a fifth of the GPU market.

Nvidia’s RTX graphics chips support ray tracing, which, the company says, can boost performance in games by 25%

Ray tracing allows developers to add more realistic-looking lighting and shadows to video games, and Nvidia continues to improve the tech in newer chips. Rendering light realistically in video games requires immense computing power, and ray tracing “cheats” the process with a little guesswork.

Since 2025, AI data center growth has created shortages and price hikes in graphics processing units and their supporting hardware, including high-speed memory.

Nvidia was founded in a Denny's restaurant

Nvidia is one of a handful of companies valued at more than $1T. In its early days, the semiconductor company sold GPUs, or graphics processing units, that were used for video games. But in recent years, Nvidia's chips have been used for crypto and AI. Nvidia has faced several setbacks on its journey, including when the US sanctioned chip exports to China.

Nvidia entered its AI era after learning that its chips could be used in industries beyond gaming

Its semiconductor chips were used in the automotive industry, for data centers, and by crypto miners. And starting in 2006, Nvidia took steps that would ultimately transform it into the AI company it's known as today. This podcast is the second part of a three-part series diving deep into Nvidia's company evolution.

Nvidia CEO Jensen Huang’s keynotes have become a main event at CES

Watch Jensen Huang’s 2025 CES keynote, where he unveiled major advances in AI and GPU technology. His presentations now draw global attention, blending product reveals with sweeping visions of the future of computing.

Nvidia's attempted Arm acquisition revealed its hopes of becoming the world's leading AI company

Though Nvidia's acquisition of Arm, another company that designs semiconductors, was ultimately quashed over antitrust concerns, the attempt offered insights into Nvidia's business strategy. Over time, Nvidia has increasingly focused on using its graphics cards to power machine learning, and its platform CUDA is dominant in the sector.

Over half of Nvidia’s revenue comes from outside the US

This chart breaks down where Nvidia’s revenue comes from—both in terms of which business segments it’s generated by and in which countries. Most of Nvidia’s revenue stems from its computing segment, which includes billions spent by companies building AI.

Watch how graphics cards are fixed in a workshop by replacing faulty components, including graphics processing units and memory, located atop printed circuit boards.

The Worldwide LHC Computing Grid—a distributed system of 2 billion gigabytes of storage across over 40 countries—manages data produced by the Large Hadron Collider.

Nvidia Omniverse can be used to accelerate 3D workflows and explore creative ideas

This video explores different apps available within Nvidia Omniverse that artists can use to create cinematic, virtual worlds. Some of the tools are AI-powered, like a feature that converts spoken audio into a video of a character speaking.

Computex Taipei is Asia’s premier computing and hardware trade show

Held annually in Taiwan, Computex Taipei brings together global tech leaders to debut innovations in processors, GPUs, AI, and next-gen computing—making it a key event for the IT and hardware industry.

Watch a microscopic tour of an Intel i486 microprocessor

Released in 1989 for $950, the chip contained over a million transistors on a piece of silicon the size of a postage stamp, much less packed than today’s chips. The layering of components makes the chip resemble a network of structures—not unlike a city—when zoomed in.

See how computer memory really works—from binary bits to modern RAM

The video explains how computers store information as 1s and 0s, how RAM functions as fast working memory, and why systems utilize different types of storage. The video demonstrates how this process enables computers to operate.

When buying a custom computer, selecting integrated graphics is more affordable, but a dedicated graphics card should be considered for CAD, video content creation, high-end gaming, and 3D modeling.

Rival companies AMD and Nvidia's CEOs are related

Advanced Micro Devices (AMD) and Nvidia are two of the world's largest manufacturers of processing chips and are fierce competitors. AMD's CEO Lisa Su and Nvidia CEO Jensen Huang are actually first cousins once removed, but reportedly don't share a close relationship and didn't even meet until later in life at an industry event.

According to the International Energy Agency, hyperscale data centers consume about as much electricity annually as 400,000 electric vehicles to provide 24/7 access to online applications, services, and information.

Quantum computers use qubits to solve problems much faster than classical machines

Unlike regular computers, where each bit is either 0 or 1, quantum computers use qubits, which can simultaneously be both—allowing for massive parallel processing power. Google’s quantum processor once solved a problem in seconds that would have taken a supercomputer 47 years.

Modular data centers can be transported on a truck for rapid deployment

Bypassing the complications involved in building larger facilities over the course of several years, such as construction costs and permits, these pre-fabricated, self-contained pods, roughly the size of shipping containers, can carry thousands of graphics processing units for small- and medium-scale AI-related tasks.

Semiconductor chips are electronic devices that store and process information

They can contain billions of microscopic switches on a chip smaller than a fingernail. An article provides a quick overview of how the devices work and how they’ve provided the backbone of the computer age.

Visualize how transformer architecture generates a large language model's outputs by running words and subwords assigned to specific numbers through multiple rounds of mathematical processing.

Nvidia CEO Jensen Huang once saved Nvidia from bankruptcy with a multi-million dollar Hail Mary

Nvidia is now known as one of the top companies contributing to the AI boom, but back in the 1990s, it almost failed. One last-ditch effort conversation that CEO Jensen Huang had with an investor saved the company.

Logic gates—digital versions of conditional statements, such as 'if 0 and 1, then 1'—are specialized circuits that combine multiple digital signals, like the binary code representing numbers, into a single output, thereby allowing machines to perform math.

The world's first commercial biological computer combines neurons with silicon

Australian start-up Cortical Labs unveiled CL1, a hybrid chip designed to help scientists study brain function in real-time. The 800,000 lab-grown neurons on the chip are kept viable for six months via a built-in life support system.

Google’s quantum computer completes a 10,000-year task in about 3 minutes

Google used its 53-qubit processor on an extremely difficult problem for traditional computers to simulate to showcase quantum supremacy. IBM later challenged Google’s claim by arguing that its Summit supercomputer could solve the same problem in just 2.5 days using a more efficient method.

Computational and biological systems share fundamental principles, like randomness

Proposed by Alan Turing and John von Neumann, the concept originates from how DNA and computers run by executing encoded instructions in parallel. The advent of AI and the temperature parameter has seen neural networks mimic the randomness of protein encoding.

Explore an interactive 3D model of the brain

The brain contains about 86 billion neurons—nervous system cells—and controls vital bodily functions via electrochemical signals. Neurons form circuits through axons, and different brain regions use these circuits to perform specific tasks.

Engineer explains VR at five levels of complexity

While its possible to grab a virtual reality headset and hop into an immersive world with little effort, a significant amount of technical and biological science has gone into creating the effect of a 3D world. This video breaks down the technology in five layers of increasing complexity.

Within fabrication plants, it takes about three months to manufacture hundreds of microchips from a single wafer of pure silicon using light and significant quantities of electricity, water, and hazardous chemicals.

How 5G will transform media and gaming experiences

The latest generation of cellular mobile communication, known as 5G, promises speeds up to 100 times faster than 4G. Intel's Asha Keddy talk to MarketWatch about how this will transform media consumption and gaming experiences.

Take a 360-degree tour of ATLAS, one of two general-purpose detectors at the Large Hadron Collider and the largest particle detector by volume ever built. (VR device-compatible)

A simple explanation of semiconductors

Semiconductor chips are electronic devices that store and process information. Today they can contain billions of microscopic switches on a chip smaller than a fingernail. The article provides a quick overview of how the devices work and how they've provided the backbone of the computer age.

Lasers help build the layers of circuit components in microchips

During the photolithography process, light is projected through a blueprint onto a light-sensitive chemical layer on silicon wafers, etching patterns that become transistors through a series of successive reactions. These processes require significant electricity, as only about 2% of photons reach the wafer.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.