Nvidia Corporation is an American 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, video games, and mobile and automotive applications.[4][5] 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 gaming, Nvidia broadened their use 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.[6][7]
In the early 2000s, the company invested over a billion dollars to develop CUDA, a software platform and API that enabled GPUs to run massively parallel programs for a broad range of compute-intensive applications.[8][9] 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.[10] 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.[11] Furthermore, it has developed the Tegra line of mobile processors for smartphones, tablets, and automotive infotainment systems.[12][13][14]
In 2023, Nvidia became the seventh U.S. company to reach a US$1 trillion valuation.[15] In 2025, driven by soaring global demand for AI data center hardware in the midst of the AI boom, Nvidia became the first company in the world to surpass US$4 trillion[16][17] and US$5 trillion in market capitalization.[18] For its strength, size and market capitalization, Nvidia has been selected as one of Bloomberg's "Magnificent Seven", the seven biggest companies on the stock market in these regards.[19]
History
Founding
Nvidia was founded on April 5, 1993,[20][21][22] 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.[23]
Corporate affairs
Leadership
Nvidia's key management as of March 2024 consists of:[187]
- Jensen Huang, founder, president and chief executive officer
- Chris Malachowsky, founder and Nvidia fellow
- Colette Kress, executive vice president and chief financial officer
- Jay Puri, executive vice president of worldwide field operations
- Debora Shoquist, executive vice president of operations
- Tim Teter, executive vice president, general counsel and secretary
Board of directors
As of January 2026, the company's board
GPU Technology Conference
Nvidia's GPU Technology Conference (GTC) is a series of technical conferences held around the world.[199] It originated in 2009 in San Jose, California, with an initial focus on the potential for solving computing challenges through GPUs.[23] In recent years, the conference's focus has shifted to various applications of artificial intelligence and deep learning; including self-driving cars, healthcare, high-performance computing, and Nvidia Deep Learning Institute (DLI) training.[200] GTC 2018 attracted over 8400 attendees.[199] GTC 2020 was converted to a digital event and drew roughly 59,000 registrants.[201] After several years of remote-only events, GTC in March 2024 returned to an in-person format in San Jose, California.[202]
Product families
Nvidia's product families include graphics processing units, wireless communication devices, and automotive hardware and software, such as:
- GeForce, consumer-oriented graphics processing products
- RTX, professional visual computing graphics processing products (replacing GTX and Quadro)
- NVS, a multi-display business graphics processor
- Tegra, a system on a chip series for mobile devices
- Tesla, line of dedicated general-purpose GPUs for high-end image generation applications in professional and scientific fields
- nForce, a motherboard chipset created by Nvidia for Intel (Celeron, Pentium and Core 2) and AMD (Athlon and Duron) microprocessors
- GRID, a set of hardware and services by Nvidia for graphics virtualization
- Shield, a range of gaming hardware including the Shield Portable, Shield Tablet and Shield TV
- Drive, a range of hardware and software products for designers and manufacturers of autonomous vehicles. The Drive PX-series
Open-source software support
Until September 23, 2013, Nvidia had not published any documentation for its advanced hardware,[212] meaning that programmers could not write free and open-source device drivers for its products without resorting to reverse engineering. Additionally, features like its compute platform CUDA and the DLSS technology suite are proprietary and only available on its hardware. As such, Nvidia has been notable for proprietization and vendor-locking practices.
Nvidia has released Linux <open-source GPU kernel modules> under dual GPL/MIT licensing, allowing developers to inspect the driver module code and contribute improvements in collaboration with the community. The Nvidia <open-gpu-kernel-modules repository> on GitHub provides the publicly accessible source code for these modules, supporting modern GPU architectures in Linux environments. Furthermore, Nvidia's acquisition of <SchedMD> and the release of new open-source AI models highlight its broader commitment to open-source software and the AI ecosystem
Instead, Nvidia provides its own binary GeForce graphics drivers for X.Org and an open-source library that interfaces with the Linux, FreeBSD or Solaris kernels and the proprietary graphics software. Nvidia also provided but stopped supporting an obfuscated open-source driver that only supports two-dimensional hardware acceleration and ships with the X.Org distribution.[213]
Nvidia proprietary software
- CUDA
- DLSS
- GameWorks
- Omniverse
- OptiX
- TensorRT (some parts open-sourced)[232]
Deep learning
Nvidia GPUs are used in deep learning, and accelerated analytics due to Nvidia's CUDA software platform and API which allows programmers to utilize the higher number of cores present in GPUs to parallelize BLAS operations which are extensively used in machine learning algorithms.[9] They were included in many Tesla, Inc. vehicles before Musk announced at Tesla Autonomy Day in 2019 that the company developed its own SoC and full self-driving computer now and would stop using Nvidia hardware for their vehicles.[233][234] These GPUs are used by researchers, laboratories, tech companies and enterprise companies.[235] In 2009, Nvidia was involved in what was called the "big bang" of deep learning, "as deep-learning neural networks were combined with Nvidia graphics processing units (GPUs)".[236] That year, the Google Brain team used Nvidia GPUs to create deep neural networks
Inception Program
Nvidia's Inception Program was created to support startups making exceptional advances in the fields of artificial intelligence and data science. Award winners are announced at Nvidia's GTC Conference. In May 2017, the program had 1,300 companies.[257] As of March 2018, there were 2,800 startups in the Inception Program.[258] As of August 2021, the program has over 8,500 members in 90 countries, with cumulative funding of US$60 billion.[259]
Controversies
GTX 970 hardware specifications advertising dispute
Issues with the GeForce GTX 970's specifications were first brought up by users when they found out that the cards, while featuring 4 GB of memory, rarely accessed memory over the 3.5 GB boundary. Further testing and investigation eventually led to Nvidia issuing a statement that the card's initially announced specifications had been altered without notice before the card was made commercially available, and that the card took a performance hit once memory over the 3.5 GB limit were put into use.[260][261][262]
The card's back-end hardware specifications, initially announced as being identical to those of the GeForce GTX 980, differed in the amount of L2 cache (1.75 MB versus 2 MB in the GeForce GTX 980) and the number of ROPs (56 versus 64 in the 980). Additionally, it was revealed that the card was designed to access its memory as a 3.5 GB section, plus a 0.5 GB one, access to the latter being 7 times slower than the first one.[263]
See also
- GPU workstations
- Nvidia Parabricks – GPU-accelerated genomics toolkit
Further reading
External links
References
- US SEC: EXHIBIT 21.1: List of Nvidia subsidiaries U.S. Securities and Exchange Commission, February 25, 2026, retrieved February 25, 2026^
- NVIDIA Logo Guidelines at a Glance nvidia.com, Nvidia, retrieved March 21, 2018^
- Timothy Prickett Morgan. Microsoft, nVidia tag team on HPC