CUDA, the computing platform that transformed graphics processing units from pixel renderers into general-purpose components powering AI, originated from a computer science student.

CUDA was Nvidia’s first step into artificial intelligence

CUDA (Compute Unified Device Architecture) is a parallel computing platform that was developed by Nvidia in the mid-2000s. By letting people perform computations in parallel, rather than sequentially, CUDA allowed people to compute large amounts of data quickly. CUDA has been used to build machine-learning models as well.

The history of graphics processing units began with hardware for displaying visuals in arcade machines, but the transition to 3D graphics led to the microchips and software that power today's AI data centers.

Take a virtual tour inside the NVIDIA GA102 graphics processing unit to explore the architecture that enables it to perform over 35 trillion calculations per second.

Nvidia pioneered the GPU industry via its origins as a video game company

From the mid-1990s to early 2000s, Nvidia carved out a niche as a maker of semiconductor chips specially designed for the video game industry. Nvidia broke through the competition to seal deals with TSMC and Microsoft, and popularized the term "graphics processing unit," aka GPU.

By 2024, Nvidia had become one of the world’s most valuable companies

Nvidia started as a graphics chip designer for PCs and consoles. This explainer traces its rise from early GPU innovation to powering AI breakthroughs and fueling the data centers behind today’s biggest tech platforms.

Nvidia entered its AI era after learning that its chips could be used in industries beyond gaming

Its semiconductor chips were used in the automotive industry, for data centers, and by crypto miners. And starting in 2006, Nvidia took steps that would ultimately transform it into the AI company it's known as today. This podcast is the second part of a three-part series diving deep into Nvidia's company evolution.

Graphics processing units, which process large amounts of data in parallel, have become the foundational hardware for AI systems that rely on hardware capable of processing millions of text parameters simultaneously.

Nvidia was founded in a Denny's restaurant

Nvidia is one of a handful of companies valued at more than $1T. In its early days, the semiconductor company sold GPUs, or graphics processing units, that were used for video games. But in recent years, Nvidia's chips have been used for crypto and AI. Nvidia has faced several setbacks on its journey, including when the US sanctioned chip exports to China.

The term GPU was first used by Sony in 1994 to refer to the geometry processing unit inside the PlayStation console, five years before Nvidia's marketing of the GeForce 256 revised the acronym to graphics processing unit.

Nvidia's attempted Arm acquisition revealed its hopes of becoming the world's leading AI company

Though Nvidia's acquisition of Arm, another company that designs semiconductors, was ultimately quashed over antitrust concerns, the attempt offered insights into Nvidia's business strategy. Over time, Nvidia has increasingly focused on using its graphics cards to power machine learning, and its platform CUDA is dominant in the sector.

Nvidia's rapid development of GPUs is forcing computing to depart from something called Moore's law

Moore’s law, named after Intel’s cofounder, predicts that the number of transistors on a chip will double every two years. But Nvidia could be outpacing that prediction.

As of 2025, Nvidia controls over 90% of the discrete graphics processing unit market, with its high-end components serving as the backbone for the AI data center boom, followed by AMD and Intel.

With many smaller computing cores, graphics processing units process many identical tasks in parallel, whereas central processing units have fewer, larger cores built to handle a wide variety of more complex tasks sequentially.

NVIDIA named several computer chip architectures after Grace Hopper

Unveiled in 2023, the GH200 Grace Hopper Superchip, designed for generative AI applications in data center-scale systems, combines a Grace CPU with a Hopper GPU to create a coherent memory model. This allows the Hopper to access Grace data directly, reducing the need to repeatedly copy data back and forth and vastly improving performance.

Watch a tech demo of the world's first consumer graphics processing unit, the Nvidia GeForce 256, released on October 11, 1999.

Nvidia CEO Jensen Huang once saved Nvidia from bankruptcy with a multi-million dollar Hail Mary

Nvidia is now known as one of the top companies contributing to the AI boom, but back in the 1990s, it almost failed. One last-ditch effort conversation that CEO Jensen Huang had with an investor saved the company.

Although GPU stands for graphics processing unit, the acronym is often used to refer to a graphics card—an assembly that houses the microchip along with supporting components.

Nvidia CEO Jensen Huang’s keynotes have become a main event at CES

Watch Jensen Huang’s 2025 CES keynote, where he unveiled major advances in AI and GPU technology. His presentations now draw global attention, blending product reveals with sweeping visions of the future of computing.

Over half of Nvidia’s revenue comes from outside the US

This chart breaks down where Nvidia’s revenue comes from—both in terms of which business segments it’s generated by and in which countries. Most of Nvidia’s revenue stems from its computing segment, which includes billions spent by companies building AI.

Nvidia Omniverse can be used to accelerate 3D workflows and explore creative ideas

This video explores different apps available within Nvidia Omniverse that artists can use to create cinematic, virtual worlds. Some of the tools are AI-powered, like a feature that converts spoken audio into a video of a character speaking.

Nvidia controls more than four-fifths of the GPU market—but isn't considered a monopoly

Semiconductor company Nvidia has a commanding market share when it comes to graphics processing units. Its competitors include Advanced Micro Devices and Intel, which together account for less than a fifth of the GPU market.

Rival companies AMD and Nvidia's CEOs are related

Advanced Micro Devices (AMD) and Nvidia are two of the world's largest manufacturers of processing chips and are fierce competitors. AMD's CEO Lisa Su and Nvidia CEO Jensen Huang are actually first cousins once removed, but reportedly don't share a close relationship and didn't even meet until later in life at an industry event.

Some graphics processing units advertised under different product names, such as Nvidia's RTX 5070-Ti and RTX 5080, are identically manufactured microchips with different defect counts.

Nvidia’s RTX graphics chips support ray tracing, which, the company says, can boost performance in games by 25%

Ray tracing allows developers to add more realistic-looking lighting and shadows to video games, and Nvidia continues to improve the tech in newer chips. Rendering light realistically in video games requires immense computing power, and ray tracing “cheats” the process with a little guesswork.

The world's first commercial biological computer combines neurons with silicon

Australian start-up Cortical Labs unveiled CL1, a hybrid chip designed to help scientists study brain function in real-time. The 800,000 lab-grown neurons on the chip are kept viable for six months via a built-in life support system.

Jensen Huang doesn't think Nvidia was a great company on day one

"Nvidia wasn't a great company on day one. We made it great over 31 years," explained businessman Jensen Huang on a recent podcast episode. He covered other principals and lessons he learned while building Nvidia in the epsiode too.

AlexNet was the AI tipping point that set the stage for ChatGPT

AlexNet is a neural network created in 2012 that laid the groundwork for modern AI models. Before AlexNet, the ideas behind neural networks existed, but the computational power to run them didn’t.

Nvidia founder Jensen Huang would not do it again

Huang was asked in 2023 whether he would do it all over again if he was transported back 30 years before. Huang said no, arguing he would not have knowingly put himself through the suffering and difficulties it took to build Nvidia, calling this ignorance to future pain one of his superpowers.

Since 2025, AI data center growth has created shortages and price hikes in graphics processing units and their supporting hardware, including high-speed memory.

Today's massive data centers evolved from single-room mainframes

Punch card computers from the late 1940s were used to store and process a company's data locally before the advent of desktop computers. As the amount of data and company sizes increased alongside the miniaturization of technology, mainframes were replaced by local server rooms and external facilities with interconnected servers running cloud-based and AI software.

Google invented the foundation of the AI movement—but ChatGPT was a shock

For years, Google was the cutting-edge of the AI world, including the development of the Transformer—the basis of contemporary AI chatbots. But since OpenAI debuted ChatGPT in 2022, the company was had to play catch-up. This is a four-hour deep dive into Google's AI history.

The game 'Counter-Strike' was developed in a dorm room

College student Minh Le designed the game through "modding," effectively building on the code of a preexisting game, the game "Half-Life" in his spare time.

Pixar's earliest days began with a handful of academics developing the technologies they thought might make fully animated movies possible.

Pixar's RenderMan software, which turned code into graphics, changed the way Hollywood produced special effects.

Married computer scientists at Stanford University built the router that became the foundation for Cisco

Len Bosack and Sandy Lerner were both computer scientists at Stanford University in 1984 when the pair created a router that connected computers on different networks. The pair asked for Stanford's permission to manufacture the router, but the university declined, and the couple decided to leave their jobs to launch Cisco, one of the tech companies that powers the internet.

Bob Noyce coinvented the integrated circuit, cofounded Intel and Fairchild Semiconductor, and is considered one of the founders of Silicon Valley

Some even called Noyce the "Mayor of Silicon Valley." According to Steve Jobs, he was the "ultimate inventor, rebel, and entrepreneur." This Founders episode dissects an article from 1983 that explains Noyce's lasting impact.

The history of the National Science Foundation Network, a precursor of the internet

Although ARPANET connected various national laboratories and supercomputing facilities by the 1970s to facilitate scientific collaboration, the system was slow and required highly trained operators. The launch of NSFNET in 1986 provided a more flexible and powerful network that went on to connect over 170 academic and research communities and link to its European counterpart, EASInet.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

Indigenous women crafted the integrated circuits for the Apollo Guidance Computer

Applying their blanket-weaving skills, over 1,000 Navajo women in New Mexico were hired to connect circuit components in intricate patterns using a microscope. Outside Boston, "rope mothers" at the Raytheon facility wove the AGC's core memory using threaded metal wires.

The AI that creates any picture you want and how it works

Since January 2021, advances in AI research produced a plethora of deep-learning models capable of generating original images from simple text prompts. Researchers at OpenAI, Google, Facebook, and others have developed yet to be released text-to-image tools, and similar models have proliferated online. This video is a primer on how we got here, how the technology works, and its implications.

Explore Anthropic Academy, which provides courses on using Claude

The online courses include Claude Code in Action, which teaches software developers how to integrate Claude Code into existing workflows; Claude 101, to learn how Claude can be used to complete everyday tasks; and an AI Fluency series to help educators, instructional designers, and students learn to apply AI in academic settings.

Linux adoption statistics, including its use in all the world's top 500 supercomputers since 2017

This has been driven by kernel-level customizations available with Linux-based operating systems that improve computational performance and reduce latency. As of 2025, Linux's desktop market share is 3.7%, but 87.8% of machine learning workloads run on Linux infrastructure. The Linux OS market is expected to grow 105.8% through 2029.

The history of the commercialization of the internet, from ARPANET to NSFNET and beyond

NSFNET was launched in 1986 by the National Science Foundation as the first network available to all researchers, connecting them to a system of supercomputer facilities. Despite growing from 2,000 computers in 1985 to 2 million by 1993, the rapid expansion of commercial internet services led to NSFNET's decommissioning by 1995.

Comparing ChatGPT with Claude, the latter excels in writing and coding projects

As of December 2025, Claude Code—Anthropic's AI-powered coding assistant—accounts for 54% of the enterprise coding market, and, while subjective, some report that Claude provides more natural-sounding writing. However, ChatGPT can create images and videos, not just generate text, and provides a broader set of applications via custom GPTs.

Neural networks mimic the structure and behavior of real physical systems

Early generative AI systems were inspired by scientific models such as the Ising model, which explains how atoms interact with one another and align in magnets. As these networks have expanded, their behavior has more closely resembled that of quantum fields, enabling them to be used in exploring complex particle physics interactions.

AI gained mainstream attention with tools like IBM Watson and Apple’s Siri

With the release of ChatGPT in 2022, which drew over 100 million weekly users in just two months, natural language processing and understanding could be achieved at scale via machine learning. Unlike earlier artificial intelligence that could pull stored knowledge, generative AI produces text, images, or sounds.

Decoding the hype around AI

2022 was the year AI broke onto the scene, with applications like ChatGPT amazing users with its ability to generate realistic-sounding answers. Technologist Arvind Narayanan presents a clear-eyed view of what the technologies can (and can't) do.

Breaking down Anthropic's products and why their models are named after literary forms

Since March 2024, the Claude series of large language models includes three varieties—Haiku, Sonnet, and Opus—named to metaphorically illustrate increasing complexity and computing power. Claude Code can perform software engineering tasks for developers and, as of January 2026, writes nearly all of Anthropic's code alongside Opus 4.5.

Google began as a Stanford University dorm room project

Launched in 1998, Google revolutionized search engines with its clean design and PageRank algorithm. As of 2025, Google has about 90% of the search market share but faces new challenges from AI-powered competitors and regulatory scrutiny.

The world's first webcam was invented in the early 1990s by scientists at Cambridge Computer Laboratory to monitor a coffee pot for researchers working in different labs on various floors.