Nvidia
In plain wordsAn American chip company whose graphics processors became the main hardware for training and running AI, making it one of the most valuable companies in the world.
An American semiconductor company whose graphics processing unitsGPU, abbr.: A computer chip first designed to draw video-game graphics that turned out to be ideal for AI, because it can do thousands of simple calculations at the same time. and software became the principal hardware platform of the deep learningdeep learning, n.: Machine learning done with neural networks built from many stacked layers: raw data such as pixels, sound or text goes in at one end, and each layer turns what it receives into something more abstract. era. Its CUDACUDA, prop. n.: Nvidia's software system for programming its graphics chips to do general calculations. Most AI software is built on it, which is a big reason Nvidia chips dominate AI. platform (2006–2007) let programmers use GPUs for general computation; AlexNetAlexNet, prop. n.: The neural network that won a major image-recognition contest by a huge margin in 2012, convincing researchers and companies that deep learning trained on graphics chips was the way forward. was trained on two Nvidia gaming cards in 2012, and the company went on to build data-center accelerators — P100, V100 (which introduced tensor corestensor core, n.: A small, specialized part of an AI chip that does one job — multiplying small blocks of numbers together — far faster than ordinary circuits can.), A100, H100H100, prop. n.: Nvidia's flagship AI chip of 2022–2024, so scarce and sought-after during the generative-AI boom that it became a unit of measure for AI computing power., and the Blackwell (2024) and Rubin (2026) generations — together with NVLinkNVLink, prop. n.: Nvidia's private high-speed connection between its AI chips, much faster than ordinary computer connections, letting dozens of chips share memory and act almost like one giant chip. and InfiniBandInfiniBand, prop. n.: A very fast networking technology used to connect the thousands of servers in supercomputers and AI training clusters, designed so data moves with minimal delay. networking, DGX systems and libraries such as cuDNN. Its software ecosystem, not only chip performance, is widely seen as its competitive moat. It designs chips but has them manufactured, chiefly by TSMCTSMC, prop. n.: The Taiwanese company that manufactures most of the world's advanced computer chips, including nearly all of the specialized chips used to train and run AI.. Analysts estimated its share of the AI accelerator market in 2025 at more than 80 percent, and in June 2024 it became, for the first time, the world's most valuable listed company. US export controls on AI chipsexport controls on AI chips, n.: Government rules that limit which countries and companies may buy the most advanced AI chips and the machines used to make them. The United States uses them mainly to slow China's progress in AI. have repeatedly restricted its sales to China. In September 2026 it agreed to acquire Hugging FaceHugging Face, prop. n.: A company whose website is the main public library for sharing AI models and datasets — often called the GitHub of machine learning — and which makes widely used free AI software. for about US$12.9 billion, announced on 3 September 2026.
The whole roadmap depends on how many Nvidia GPUs we can get next quarter.
Origin
Founded on 5 April 1993 by Jensen Huang, Chris Malachowsky and Curtis Priem, who planned the company at a Denny's restaurant in San Jose, California, to make graphics chips for games and multimedia; its GeForce 256 (1999) was marketed as the first 'GPU'. Huang has been chief executive since the founding. It acquired the networking firm Mellanox in 2020.
Usage note
Officially styled NVIDIA. Its main competitors in AI accelerators are AMD's GPUs and custom chips such as Google's TPUTPU, abbr.: Google's own family of chips built specifically for AI, used to train and run its Gemini models and rented to other companies through its cloud..
Related
- GPUGPU, abbr.: A computer chip first designed to draw video-game graphics that turned out to be ideal for AI, because it can do thousands of simple calculations at the same time.
- CUDACUDA, prop. n.: Nvidia's software system for programming its graphics chips to do general calculations. Most AI software is built on it, which is a big reason Nvidia chips dominate AI.
- H100H100, prop. n.: Nvidia's flagship AI chip of 2022–2024, so scarce and sought-after during the generative-AI boom that it became a unit of measure for AI computing power.
- Jensen HuangJensen Huang, prop. n.: The Taiwanese-American co-founder and chief executive of Nvidia, whose graphics chips became the main engine for training and running modern AI.
- TSMCTSMC, prop. n.: The Taiwanese company that manufactures most of the world's advanced computer chips, including nearly all of the specialized chips used to train and run AI.
- export controls on AI chipsexport controls on AI chips, n.: Government rules that limit which countries and companies may buy the most advanced AI chips and the machines used to make them. The United States uses them mainly to slow China's progress in AI.
- AI acceleratorAI accelerator, n.: Any chip built or tuned to speed up AI — graphics chips, Google's TPUs, phone 'neural engines' and many startup designs. Such chips do the heavy math of training and running models.
- tensor coretensor core, n.: A small, specialized part of an AI chip that does one job — multiplying small blocks of numbers together — far faster than ordinary circuits can.
Mentioned in
export controls on AI chipsexport controls on AI chips, n.: Government rules that limit which countries and companies may buy the most advanced AI chips and the machines used to make them. The United States uses them mainly to slow China's progress in AI., Hugging FaceHugging Face, prop. n.: A company whose website is the main public library for sharing AI models and datasets — often called the GitHub of machine learning — and which makes widely used free AI software., Jensen HuangJensen Huang, prop. n.: The Taiwanese-American co-founder and chief executive of Nvidia, whose graphics chips became the main engine for training and running modern AI., AI factoryAI factory, n.: A data center built to turn electricity and data into AI models and AI answers at industrial scale; in Europe, also a public supercomputing hub that helps companies and researchers build AI., AMDAMD, prop. n.: An American chip company that makes computer processors and graphics chips, and Nvidia's main rival in the chips used for artificial intelligence., compute governancecompute governance, n.: Using control over the computer chips and data centers that AI depends on — who can buy them, where they go, how they are used — as a way to oversee powerful AI., HuaweiHuawei, prop. n.: A Chinese technology giant, known for telecom equipment and phones, that designs China's leading homegrown AI chips despite US trade restrictions., TSMCTSMC, prop. n.: The Taiwanese company that manufactures most of the world's advanced computer chips, including nearly all of the specialized chips used to train and run AI., CerebrasCerebras, prop. n.: An American chip company that makes a single giant processor the size of a whole silicon wafer, used to train and run AI models very quickly., CoreWeaveCoreWeave, prop. n.: An American company that rents out huge numbers of Nvidia graphics processors over the internet to AI developers such as Microsoft, OpenAI and Meta., MLCommonsMLCommons, prop. n.: An industry and university consortium that runs MLPerf, the standard tests of how fast computers train and run AI models, as well as benchmarks of AI safety., TensorRTTensorRT, prop. n.: NVIDIA's software for making a trained neural network run as fast as possible on NVIDIA graphics chips, by merging and simplifying its calculations and testing which way of doing each one is quickest.