In January 1993, three engineers met at a Denny's in San Jose and argued about the future of computing. Jensen Huang had been designing microprocessors at LSI Logic and AMD. Chris Malachowsky and Curtis Priem had built graphics hardware at Sun Microsystems and IBM. Their shared conviction was narrow and, at the time, unfashionable: that a special kind of chip built to do many calculations at once could unlock things ordinary processors never would. They named the company after invidia, the Latin word for envy - the reaction they hoped their technology would provoke.

For most of its life, NVIDIA was known to the public as a maker of graphics cards for video games. That business was real and lucrative. But the more consequential decision came in 2006, when the company released CUDA, a software platform that let programmers use its graphics chips for general computation rather than just drawing pixels. It was a bet with no obvious customer. For years, CUDA was a curiosity. Then machine-learning researchers discovered that the same math that renders a game frame - millions of small operations running in parallel - is exactly what training a neural network requires.

What NVIDIA actually does

Strip away the jargon and NVIDIA does one thing: it builds hardware and software for computing that runs in parallel. Where a traditional processor handles instructions largely one after another, a graphics processing unit, or GPU, splits work into thousands of simultaneous threads. That design was born to render 3D scenes. It turns out to be the natural shape of modern artificial intelligence, scientific simulation, and data analytics.

The company sells this capability at every scale. A gamer buys a single GeForce card. A cloud provider buys server racks packed with data-center GPUs, stitched together by NVIDIA's own networking into machines that behave like one enormous computer. Automakers license the DRIVE platform for self-driving systems. Factories use Omniverse to build digital twins of their operations. Running underneath all of it is CUDA and a stack of software that has become, for a generation of developers, the default language of accelerated computing.

Who its customers are

NVIDIA's customer list reads like a map of the modern technology economy. The largest cloud providers - Amazon Web Services, Microsoft Azure, Google Cloud, and Oracle - buy its chips by the hundreds of thousands to rent out as AI infrastructure. The AI labs pushing the frontier, including OpenAI, Anthropic, Meta, and Mistral AI, train and serve their models on NVIDIA systems. Beyond them sit enterprises, universities, research labs, automakers, and hundreds of millions of gamers and creators. And beneath every one of those relationships is a quieter constituency: the millions of developers who have learned to write CUDA and have no easy way to switch.

In its most recent quarter the company reported roughly $81.6 billion in revenue, up about 85 percent from a year earlier, with more than 90 percent of it coming from the data-center segment. Gaming built NVIDIA. Artificial intelligence transformed the scale of it.

The problems it solves

The central problem is simple to state and brutally hard to solve: modern AI models are enormous, and training or running them demands more computation than any single conventional processor can deliver in a reasonable time or budget. NVIDIA's answer is to make that computation faster, cheaper per unit of work, and possible at all. Its newest platforms are pitched almost entirely in those terms - not as raw speed, but as cost per token, the price of producing a unit of AI output. The company says its Vera Rubin platform can lower that cost up to tenfold compared with the prior generation.

For a research team, that difference decides whether an experiment is affordable. For a company deploying AI to customers, it decides whether the product has a viable margin. NVIDIA sells, in effect, the economics of artificial intelligence.

How it differs from competitors

NVIDIA is far from the only company that makes AI chips. AMD builds competitive GPUs, Intel has its own accelerators, and the largest cloud providers design custom silicon - Google's TPUs, Amazon's Trainium - to reduce their dependence on any single supplier. A wave of startups such as Cerebras and Groq chase specialized corners of the market.

What separates NVIDIA is less any single chip than the surrounding system. CUDA and its ecosystem represent nearly two decades of accumulated software, tools, and developer habit. Code written for an older NVIDIA GPU generally runs unmodified on newer ones - a backward compatibility the company treats as a strategic asset. Rivals can match a chip's specifications; matching an ecosystem that millions of engineers already know is a slower proposition. NVIDIA also sells complete systems - chips, high-speed networking acquired with Mellanox, and full server racks - rather than components, positioning itself as a supplier of finished AI supercomputers.

Products, services, and business model

NVIDIA is a fabless company: it designs chips and reference systems but outsources manufacturing to foundries, chiefly TSMC. Its product range spans GeForce cards for consumers, data-center GPUs such as the Blackwell and Rubin generations for AI, the DRIVE platform for vehicles, and Omniverse for industrial simulation. Increasingly it packages these as platforms rather than parts, layering enterprise AI software and developer tools on top of the silicon. That software both adds recurring value and deepens the reasons customers return for the next hardware cycle.

The expertise behind all of this is unusually concentrated. NVIDIA has spent three decades on one discipline - parallel, accelerated computing - under the same founding chief executive. Jensen Huang has led the company since 1993, a tenure that spans a near-bankruptcy in its early years, the rise and fall of crypto-mining demand, and now the AI supercycle. That institutional memory shows up in the company's habit of investing in capabilities, like CUDA, years before a market exists to justify them.

Where it fits in the market

NVIDIA sits at the center of the semiconductor industry and, by extension, the AI economy built on top of it. When Huang told the audience at the company's GTC 2026 conference that NVIDIA saw roughly a trillion dollars in orders for its Blackwell and Rubin platforms through 2027, the figure was less a boast than a description of how much of the industry's near-term spending now flows through one supplier. Its market value has climbed above $5 trillion, among the highest ever recorded for a public company.

That position carries obvious risk. Concentration invites competition, regulation, and the ambitions of customers who would rather build their own chips. But for now the practical reality is straightforward: much of the world's artificial intelligence is trained and served on NVIDIA hardware, written in NVIDIA's software, and networked with NVIDIA's interconnect. A company that began by chasing the envy of the graphics industry ended up building the machinery that a much larger industry now depends on.