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computer science

Nvidia

Nvidia is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Nvidia rather than just read about it. In short: Nvidia Corporation ( en-VID-ee-ə) is an American multinational technology company headquartered in Santa Clara, California. The company develops graphics processing units (GPUs), systems on chips (SoCs), and application programming interfaces (APIs) for data science, high-performance computing, artificial intelligence (AI), and mobile and automotive applications.

Nvidia — main illustration
Nvidia — illustration

Key takeaways

  • Nvidia belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Nvidia to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Nvidia from memory before moving on to harder problems.

Reference excerpt

Nvidia Corporation ( en-VID-ee-ə) is an American multinational technology company headquartered in Santa Clara, California. The company develops graphics processing units (GPUs), systems on chips (SoCs), and application programming interfaces (APIs) for data science, high-performance computing, artificial intelligence (AI), and mobile and automotive applications. Founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem, Nvidia has been widely described as a Big Tech company. Originally focused on GPUs for video games, Nvidia quoted themselves as a "full-stack" computing enterprise. They broadened their usage into other markets, including artificial intelligence (AI), professional visualization, and supercomputing. The company's product lines include GeForce GPUs for gaming and creative workloads, and professional GPUs for edge computing, scientific research, and industrial applications. As of the first quarter of 2025, Nvidia held a 92% share of the discrete desktop and laptop GPU market.

History

Founding

Nvidia was founded on April 5, 1993, by Jensen Huang, a Taiwanese-American electrical engineer who was previously the director of CoreWare at LSI Logic and a microprocessor designer at AMD; Chris Malachowsky, an engineer who worked at Sun Microsystems; and Curtis Priem, who was previously a senior staff engineer and graphics chip designer at IBM and Sun Microsystems. In late 1992, the three men agreed to start the company by sketching out their initial ideas in a meeting at a Denny's roadside diner on Berryessa Road in East San Jose. At the time, Malachowsky and Priem were frustrated with Sun's management and were looking to leave, but Huang was on "firmer ground", in that he was already running his own division at LSI. The three co-founders discussed a vision of the future for gaming and multimedia markets, which was so compelling that Huang decided to leave LSI and become the chief executive officer of their new startup. The three co-founders envisioned 3D graphics-based processing as the best trajectory for tackling challenges that had eluded general-purpose computing methods. As Huang later explained: "We also observed that video games were simultaneously one of the most computationally challenging problems and would have incredibly high sales volume. Those two conditions don't happen very often. Video games was our killer app – a flywheel to reach large markets funding huge R&D to solve massive computational problems." The first problem was who would quit first. Huang's wife, Lori, did not want him to resign from LSI unless Malachowsky resigned from Sun at the same time, and Malachowsky's wife, Melody, felt the same way about Huang. Priem broke that deadlock by resigning first from Sun, effective December 31, 1992. According to Priem, this put pressure on Huang and Malachowsky to not leave him to "flail alone", so they gave notice too. Huang left LSI and "officially joined Priem on February 17", which was also Huang's 30th birthday, while Malachowsky left Sun in early March. In early 1993, the three founders began working together on their new startup in Priem's townhouse in Fremont, California. With $40,000 in the bank (equivalent to $89,000 in 2025), the company was born. The company subsequently received $20 million of venture capital funding from Sequoia Capital, Sutter Hill Ventures, and others. During the late 1990s, Nvidia was one of 70 startup companies pursuing the idea that graphics acceleration for video games was the path to the future. Only two survived: Nvidia and ATI Technologies, the latter of which merged into AMD. Nvidia initially had no name. Priem's first idea was "Primal Graphics", a syllabic abbreviation of two of the founders' last names, but that left out Huang. They soon discovered it was impossible to create a workable name with syllables from all three founders' names, after considering "Huaprimal", "Prihuamal", "Malluapri", etc. The next idea came from Priem's idea for the name of Nvidia's first product. Priem originally wanted to call it the "GXNV", as in the "next version" of the GX graphics chips which he had worked on at Sun. Then Huang told Priem to "drop the GX", resulting in the name "NV". Priem made a list of words with the letters "NV" in them. At one point, Malachowsky and Priem wanted to call the company NVision, but that name was already taken by a manufacturer of toilet paper. Both Priem and Huang have taken credit for coming up with the name Nvidia, from "invidia", the Latin word for "envy". After the company outgrew Priem's townhouse, its original headquarters office was in Sunnyvale, California. In the early-to-mid 2000s, the company invested over a billion dollars to develop CUDA, a software platform and API (Application Programming Interfaces) that enabled GPUs to run massively parallel programs for a broad range of compute-intensive applications. As a result, as of 2025, Nvidia controlled more than 80% of the market for GPUs used in training and deploying AI models, and provided chips for over 75% of the world's TOP500 supercomputers. The company has also expanded into gaming hardware and services, with products such as the Shield Portable, Shield Tablet, and Shield TV, and operates the GeForce Now cloud gaming service. Furthermore, it has developed the Tegra line of mobile processors for smartphones, tablets, and automotive infotainment systems.

… excerpt ends here. Continue reading the full article.

Illustrations

Nvidia illustration
Nvidia illustration
Nvidia: The Denny's roadside diner in San Jose, California, where Nvidia's three co-founders agreed to start the company in late 1992
The Denny's roadside diner in San Jose, California, where Nvidia's three co-founders agreed to start the company in late 1992
Nvidia: Nvidia's former headquarters which was home to the company through most of its pre-AI period (still in use)
Nvidia's former headquarters which was home to the company through most of its pre-AI period (still in use)
Nvidia: Aerial view of Endeavor, the first of the two new Nvidia headquarters buildings, in Santa Clara, California, in 2017
Aerial view of Endeavor, the first of the two new Nvidia headquarters buildings, in Santa Clara, California, in 2017

Worked examples

Example 1 — a first encounter with Nvidia

Start with the simplest possible case. Write down what Nvidia claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Nvidia before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Nvidia ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Nvidia

In research
Nvidia appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Nvidia in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Nvidia is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1993 establishments in California, 1999 initial public offerings, American companies established in 1993, so understanding it makes those chapters shorter.
In everyday life
Look for Nvidia outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Nvidia in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Nvidia means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Nvidia out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Nvidia in simple terms?

Nvidia Corporation ( en-VID-ee-ə) is an American multinational technology company headquartered in Santa Clara, California. The company develops graphics processing units (GPUs), systems on chips (SoCs), and application programming interfaces (APIs) for data science, high-performance computing, art…

Why does Nvidia matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Nvidia?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Nvidia.

Tags

  • 1993 establishments in California
  • 1999 initial public offerings
  • American companies established in 1993
  • Artificial intelligence industry in the United States
  • Companies based in Santa Clara, California
  • Companies in the Dow Jones Global Titans 50
  • Companies in the Dow Jones Industrial Average
  • Companies in the Nasdaq-100
  • Companies involved in the Gaza war
  • Companies listed on the Nasdaq
  • Computer companies established in 1993
  • Computer companies of the United States

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