Names, versions and owners change fast. Checked October 2026.
In plain words
Nvidia does not make a chatbot. It makes the hardware underneath. The company, based in Santa Clara, California, was founded in 1993 to make graphics chips for video games. Those chips, called GPUs, are built to do thousands of small calculations at the same time.
That turned out to be what AI needs. Training a neural network means multiplying huge grids of numbers again and again, and a GPU does that far faster than an ordinary processor. In 2012 a landmark image-recognition network, AlexNet, was trained on Nvidia GPUs, and AI work has leaned on them ever since.
Nvidia's other strength is software. Its CUDA platform, launched in 2006, lets programmers use GPUs for general computing, and many AI tools are written to work with it. Today Nvidia sells complete AI systems for data centres. Each chip generation has its own name; the box above shows the current ones.
Everyday example
A highway versus a single lane
Imagine moving a crowd from a stadium after a cricket match. One very fast car on a single lane would take forever, trip after trip. A highway with thousands of lanes, each with a modest car, clears it in minutes. An ordinary processor is the fast single car. An Nvidia GPU is the many-lane highway, and AI training is the stadium crowd.
Where the example breaks: Cars on a highway each carry different people to different places. A GPU's many cores mostly do the same kind of calculation on different numbers at once, which suits AI maths but not every computing job.
Latestas of October 2026
- Data-centre chips
- Blackwell generation, with its successor Vera Rubin, which Nvidia said on 26 August 2026 was ramping into full production
- Hugging Face deal
- Agreed on 3 September 2026 to buy Hugging Face for about $12.9 billion; closing expected in the first half of 2027, subject to approval
- Chief executive
- Jensen Huang, a co-founder
These details change often. The rest of this page explains what it is, which stays true.
How it actually works
Nvidia's role in AI comes from three layers working together.
Chips built for parallel maths
A GPU holds thousands of simple cores. AI models are mostly parameters multiplied with inputs, and those multiplications can be split across all the cores at once.
Software that ties it together
CUDA, and the libraries built on it, let popular AI frameworks run on Nvidia GPUs without programmers handling the hardware directly. This software head start is a big reason AI code tends to target Nvidia.
- Founded
- 5 April 1993 by Jensen Huang, Chris Malachowsky and Curtis Priem
- Headquarters
- Santa Clara, California
- Key software
- CUDA, unveiled in 2006
- Main AI products
- Data-centre GPUs, networking and complete rack systems, plus CUDA software
Nvidia supplies hardware and software to AI makers rather than making a chatbot of its own. See GPU and compute.
Questions people ask
Why is Nvidia important for AI?
Because its GPUs, together with its CUDA software, are widely used to train and run AI models. GPUs do thousands of calculations at once, which is exactly what training and running a neural network requires.
Does Nvidia make ChatGPT?
What is a GPU?
A graphics processing unit: a chip first designed to draw video-game graphics, which needs many small calculations at once. That same design makes it well suited to AI. See GPU for more.
Can Nvidia sell its AI chips everywhere?
No. Since October 2022, United States export rules have limited sales of Nvidia's most advanced AI chips to China, and Nvidia has made modified versions to fit those rules. The rules have changed several times since.