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

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 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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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’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.

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.

Jensen Huang doesn't think Nvidia was a great company on day one

"Nvidia wasn't a great company on day one. We made it great over 31 years," explained businessman Jensen Huang on a recent podcast episode. He covered other principals and lessons he learned while building Nvidia in the epsiode too.

AlexNet was the AI tipping point that set the stage for ChatGPT

AlexNet is a neural network created in 2012 that laid the groundwork for modern AI models. Before AlexNet, the ideas behind neural networks existed, but the computational power to run them didn’t.

Nvidia founder Jensen Huang would not do it again

Huang was asked in 2023 whether he would do it all over again if he was transported back 30 years before. Huang said no, arguing he would not have knowingly put himself through the suffering and difficulties it took to build Nvidia, calling this ignorance to future pain one of his superpowers.

AI expert shows the cyclical nature of AI hype—in 1988

Rodney Brooks wrote several decades ago about the boom and bust cycles of artificial intelligence. Starting from the 1950s excitement over ENIAC's multiplication through the second launch of then-novel neural networks, Brooks suggests AI progresses incrementally despite the hype.

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.

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

The term 'artificial intelligence' was coined in 1956

Dartmouth professor John McCarthy introduced the term during a summer workshop aimed at exploring how to make machines perform human-like tasks. Two years later, Perceptron Mark I became one of the first neural network machines.

FLOW-MATIC was the first programming language resembling the English language

As part of the team working on UNIVAC—the first commercially produced electronic digital computer in the US—Grace Hopper helped develop the system that enabled nonprogrammers and businesspeople without a strong mathematics background to use the machine effectively, thereby enhancing the computer's commercial viability.

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.

Transformer architecture was the breakthrough that made AI chatbots possible

It changed how text was translated from a sequential, "one word at a time" method to one where every word in a text is processed in parallel. Advances in positional encoding and self-attention also helped models recognize context and word order better.

The first compiler to facilitate communication with computers was developed by Grace Hopper

The A-0 program linker automatically translated mathematical symbols into machine-readable binary code. Although it took two years for computer executives to get on board with the idea, the technology was foundational to future programming languages that allowed computers to understand commands written in word-based languages.

Today's massive data centers evolved from single-room mainframes

Punch card computers from the late 1940s were used to store and process a company's data locally before the advent of desktop computers. As the amount of data and company sizes increased alongside the miniaturization of technology, mainframes were replaced by local server rooms and external facilities with interconnected servers running cloud-based and AI software.