NVIDIA: the company building the engines of artificial intelligence
From video games to enormous AI data centers, how a Silicon Valley chip designer became one of the most important companies behind modern computing.
Imagine asking an artificial intelligence program to write a letter, translate a document, or explain something complicated. A few seconds later, an answer appears.
It feels almost effortless. But behind that simple exchange, computers are performing enormous amounts of mathematics, processing numbers and comparing patterns at remarkable speed.
Much of that work depends on technology developed by NVIDIA.
NVIDIA does not operate ChatGPT or build every artificial intelligence system. Instead, it designs powerful processors, networking equipment, computer systems, and software that AI developers use to build and run their technology.
The company calls its approach accelerated computing: using specialized processors to handle demanding calculations more efficiently than conventional computers working alone.
The interesting part is that artificial intelligence was not what NVIDIA's founders originally had in mind.
They were trying to make video games look better.
It Started With Video Games
NVIDIA was founded in 1993 by three engineers: Jensen Huang, Chris Malachowsky, and Curtis Priem.
Their focus was computer graphics, particularly the technology needed to create realistic three-dimensional images.
Computer games were becoming more visually ambitious. Developers wanted detailed landscapes, realistic lighting, and characters that moved smoothly. Producing those images required millions of calculations.
NVIDIA saw an opportunity to develop specialized chips to handle that work.
In 1999, it introduced the GeForce 256, which it marketed as the world's first graphics processing unit, or GPU. Graphics accelerators already existed, but NVIDIA helped establish the GPU as a distinct category of computer processor.
To understand why that mattered, it helps to understand how an ordinary computer works.
The Difference Between a CPU and a GPU
Every computer needs a processor to carry out instructions.
The traditional processor is called a central processing unit, or CPU. It acts as the computer's general-purpose control center, handling everything from opening programs to running operating systems.
A CPU contains processing cores, individual units capable of carrying out instructions. Modern CPUs are designed to handle many different kinds of tasks.
But some jobs require a different approach.
Imagine a restaurant kitchen.
A CPU is like a small team of highly skilled chefs. Each can prepare complicated dishes, change recipes, and handle unexpected requests.
A GPU is more like a large kitchen organized around preparing thousands of similar items simultaneously.
The individual workers may be less flexible, but when the job involves repeating the same operation many times, the larger team can accomplish an enormous amount of work.
That is the principle behind parallel processing, which means performing many calculations at once.
GPUs contain large numbers of processing units designed to carry out similar mathematical operations together.
This makes them particularly useful for computer graphics, scientific simulations, and artificial intelligence.
Producing a realistic computer image requires calculating the appearance of millions of individual points. Many of those calculations can happen independently.
Artificial intelligence presents a similar opportunity because much of its work involves repeatedly multiplying and adding large groups of numbers.
CPUs remain essential. GPUs do not simply replace them. The two processors have different strengths and frequently work together.
The Discovery That Changed NVIDIA
For years, NVIDIA's processors were primarily associated with graphics.
But researchers realized that the same hardware could solve mathematical problems unrelated to pictures.
The difficulty was programming it.
Graphics processors had been designed to follow instructions associated with drawing images. Scientists wanting to use them for other calculations often had to work around those restrictions.
NVIDIA addressed this problem in 2006 with CUDA.
CUDA, short for Compute Unified Device Architecture, is a computing platform and programming system that allows developers to use NVIDIA GPUs for general mathematical calculations.
In simpler terms, CUDA gave programmers a practical way to tell a graphics processor to do something other than draw pictures.
A scientist could simulate physical processes. An engineer could analyze complicated designs. An AI researcher could train a computer model.
CUDA also includes specialized software libraries, collections of ready-made instructions that developers can reuse rather than writing everything themselves.
This changed NVIDIA's business. It was no longer designing chips exclusively for people who needed better graphics. It was building a computing platform for many industries.
CUDA also explains why NVIDIA cannot be understood simply by examining its chips.
A processor is only useful when software can tell it what to do. Over time, NVIDIA developed tools that allow programmers to build applications around its hardware.
Changing to another manufacturer's processor may therefore involve more than replacing a physical component. Developers may also need to adapt their software.
That combination of hardware and software has become an important part of NVIDIA's position in computing.
Why Artificial Intelligence Needs So Much Computing Power
Artificial intelligence can sound mysterious, but much of the underlying work comes down to mathematics.
Consider a computer learning to recognize pictures of cats.
Instead of receiving a simple list of rules describing a cat, the system examines large collections of examples.
It gradually adjusts numerical settings inside a mathematical model to become better at recognizing patterns.
A model is a collection of mathematical relationships that a computer uses to process information.
Many modern AI systems use neural networks, mathematical structures loosely inspired by the way biological neurons transmit signals.
These networks contain adjustable numbers called parameters. During training, the system repeatedly changes those parameters to improve its results.
This process can require extraordinary numbers of calculations, particularly multiplying large tables of numbers called matrices.
GPUs are especially well suited to this repetitive mathematical work.
NVIDIA's technology played a role in a landmark development in 2012, when the AlexNet neural network demonstrated the effectiveness of GPU-powered deep learning for image recognition.
Deep learning is a form of machine learning that uses neural networks with multiple layers of mathematical processing.
The same computing approach now supports systems that generate text, analyze images, recognize speech, and perform scientific calculations.
There are two especially important stages.
The first is training, when an AI model learns patterns from large amounts of data.
The second is inference, when a trained model processes a new request and produces an answer.
Training is like preparing a student for an examination. Inference is like the student answering a question using what was learned.
Both require computing power, although their demands can differ considerably.
NVIDIA designs processors and software for both stages.
Inside an NVIDIA AI Chip
Modern NVIDIA data-center processors are very different from the graphics chips the company sold in the 1990s.
Consider Blackwell, a processor architecture introduced in 2024.
An architecture is the underlying design of a processor: how its components are organized and work together.
NVIDIA says its original Blackwell GPU design contains 208 billion transistors.
A transistor is a microscopic electronic switch. Modern chips contain enormous numbers of them, arranged into circuits that perform calculations and control information.
NVIDIA's Blackwell design combines two pieces of silicon, called dies, into a single GPU package. The pieces communicate through an extremely fast internal connection.
The chips use a specialized manufacturing process developed by Taiwan Semiconductor Manufacturing Company, commonly called TSMC.
But transistor count tells only part of the story.
Modern NVIDIA AI processors contain specialized circuits called Tensor Cores, designed to accelerate the matrix calculations used in artificial intelligence.
Memory is equally important.
A processor needs quick access to the numbers it is working with. If it spends too much time waiting for information, its calculating power goes unused.
NVIDIA's high-end AI systems use high-bandwidth memory, or HBM, designed to move large amounts of information between memory and the processor extremely quickly.
This matters when running large AI models containing enormous collections of numerical parameters.
The challenge is not merely calculating quickly. It is keeping information moving fast enough for those calculations to remain useful.
When One Chip Is Not Enough
Even a powerful GPU has limits. The largest AI projects require many processors working together.
That brings us to data centers.
A data center is a building filled with computer servers, storage equipment, networking systems, and machinery to supply electricity and remove heat.
A server is a computer designed to provide computing services to other computers or users.
Large AI servers may contain several specialized processors, while a data center may contain thousands of servers.
The challenge is getting them to cooperate.
Imagine a construction project with thousands of workers. Having more workers helps only if materials arrive on time and everyone can communicate efficiently.
The same applies to processors.
NVIDIA develops networking technology that allows GPUs to exchange information rapidly.
Its NVLink technology provides high-speed connections between processors. The company also supplies networking equipment using Ethernet and InfiniBand.
Ethernet is widely used in computer networks. InfiniBand is designed for demanding computing environments requiring fast communication with minimal delays.
One Blackwell system, the GB200 NVL72, combines 72 Blackwell GPUs and 36 Grace CPUs in a liquid-cooled computing rack.
A rack is a tall cabinet holding computing equipment. Liquid cooling uses circulating fluid to remove heat from its components.
The processors are connected to work as one tightly coordinated computing system.
NVIDIA's newer Vera Rubin platform takes the approach further.
Introduced in 2026, it combines processors, networking equipment, and software into a coordinated system.
NVIDIA says a Vera Rubin NVL72 rack contains 72 Rubin GPUs and 36 Vera CPUs.
The company reports substantial performance improvements over earlier systems, although these are NVIDIA's own measurements and depend on the workloads being tested.
NVIDIA Does Not Manufacture Most of Its Chips
There is a surprising detail about NVIDIA's hardware operation.
The company designs its processors, but generally does not manufacture the silicon chips itself.
Instead, it works with specialized semiconductor manufacturers.
A semiconductor is a material, usually silicon in modern processors, whose electrical properties can be controlled to create electronic circuits.
The factories that manufacture these chips are called fabrication plants, or fabs.
NVIDIA uses manufacturing partners including TSMC and Samsung. Its annual report also identifies SK Hynix, Micron, and Samsung as memory suppliers.
Other companies handle assembly, testing, and packaging, the process of connecting semiconductor components and preparing them for use in finished systems.
This arrangement is called a fabless business model.
NVIDIA concentrates on designing technology while specialized manufacturers operate the complicated factories needed to produce it.
But the arrangement creates dependencies.
NVIDIA cannot simply decide to manufacture unlimited additional chips. Production depends on factory capacity, specialized packaging equipment, memory supplies, and other components.
In its 2026 annual report, NVIDIA acknowledged that manufacturing lead times for certain supplies had sometimes exceeded 12 months.
It also identified shortages, production delays, and the geographic concentration of manufacturing as operational risks.
How Large Has NVIDIA Become?
The scale of NVIDIA's business is difficult to appreciate until the numbers are put together.
According to NVIDIA, it generated approximately $215.9 billion in revenue during fiscal 2026, the accounting year ending January 25, 2026.
Revenue is the money a company receives from selling products and services before deducting expenses.
Of that total, approximately $193.7 billion came from its data-center business, roughly 90% of annual revenue.
Gaming, the industry that originally established NVIDIA, generated approximately $16 billion.
The operation continued expanding during 2026.
For the quarter ending July 26, NVIDIA reported revenue of $96.2 billion, including $89 billion from data centers.
Those figures illustrate how dramatically the company's business has shifted toward supplying AI computing infrastructure.
Its workforce is also substantial.
At the end of fiscal 2026, NVIDIA reported approximately 42,000 employees across 38 countries.
Around 31,000 worked in research and development, the engineering and scientific work involved in creating and improving products.
Another 11,000 worked in sales, marketing, operations, and administrative positions.
NVIDIA also reported that more than 80% of its employees held technical positions.
These numbers help explain what NVIDIA has become: not simply a chip designer, but a large engineering organization developing processors, networking systems, software, and complete computing platforms.
Beyond Artificial Intelligence
Although data centers dominate NVIDIA's business, its technology reaches other industries.
GeForce graphics processors remain important products for computer gaming.
NVIDIA's RTX technology includes ray tracing, a technique that simulates the behavior of light to produce realistic images.
The company also develops DLSS, an AI-assisted graphics technology that helps games produce detailed images while reducing some of the computing work required.
Professional designers use NVIDIA processors for three-dimensional modeling, engineering, animation, and visual effects.
The company also develops technology for robotics and vehicles.
Its DRIVE platform provides computing hardware and software for automotive applications, including automated driving systems.
Omniverse, another NVIDIA platform, supports the creation and simulation of three-dimensional environments.
These tools allow engineers to experiment with digital representations of machines, factories, and other physical systems.
The Challenges Behind the Technology
NVIDIA's position in artificial intelligence is substantial, but the industry is not standing still.
Other semiconductor companies develop competing processors. Large cloud-computing companies are also designing specialized chips for their own AI systems.
Competition involves more than processing speed.
Customers must consider electricity consumption, software compatibility, memory capacity, networking performance, and the cost of operating an entire computing system.
Energy presents another challenge.
Large data centers require substantial electricity, and their processors generate heat that must be removed.
NVIDIA acknowledges that limitations involving electricity, water, and data-center construction can affect deployment of its products.
International trade restrictions also complicate the business.
The United States has imposed export controls on certain advanced computing technologies, affecting NVIDIA's ability to sell some processors to China and other destinations.
These restrictions illustrate how semiconductor technology has become intertwined with international policy.
From Pictures to Intelligence
There is something remarkable about the path NVIDIA has followed.
A company established to improve the appearance of computer games now designs machinery used to train artificial intelligence, simulate scientific problems, and operate enormous computing centers.
The connection between those activities is mathematics.
The same ability to perform many calculations simultaneously, originally valuable for drawing complicated pictures, turned out to be useful for an entirely different generation of computing problems.
But NVIDIA's transformation involved more than discovering new uses for an existing chip.
It required developing programming tools, specialized processors, memory systems, networking equipment, and the engineering expertise needed to make everything work together.
That is why understanding NVIDIA requires looking beyond the GPU itself.
The company's products now extend from individual processors to entire racks of interconnected machines, supported by software that developers use to build their own applications.
Artificial intelligence may be what people see on their screens. NVIDIA's business is largely concerned with the machinery operating behind those screens.
And as computers take on increasingly complicated tasks, the engineering challenge remains much the same as when NVIDIA began: finding a faster, more practical way to turn enormous numbers of calculations into something useful.
