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.

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.

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.

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

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.

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.

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.

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.

Unlike generative AI systems, such as chatbots that do not take follow-up actions after generating their output, agentic AI systems proactively take steps to achieve goals without continuous user input.

In 2023, OpenAI spent $520 million on ChatGPT

The ongoing development and refinement of new AI models requires larger datasets and processing more parameters between words and subwords to improve the accuracy of generated content. New startups are expected to harness this technology to provide unthought-of products and services.

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.

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.

Transformer architecture was the breakthrough that made AI chatbots possible

It changed how text was translated from a sequential, "one word at a time" method to one where every word in a text is processed in parallel. Advances in positional encoding and self-attention also helped models recognize context and word order better.

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.

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.

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 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.

ChatGPT is trained on most of the world's digitally accessible text

As with other AI chatbots, ChatGPT is primarily trained through non-supervised pretraining, which identifies underlying structure and patterns within input data. Then, human intervention addresses harmful or biased patterns that should not be propagated to generated outputs.

How ChatGPT learns to read

The introduction of ChatGPT last fall jumpstarted public interest in the future capabilities of artificial intelligence and what it means for society. Of many somewhat "jargony" terms thrown around, the phrase "Large Language Model" is commonly seen—but also incredibly unclear as to what it means. In the simplest sense, the programs comb through mountains of internet data in response to a question and guess at what should come next (subscription needed).

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.

Tokens—numbers assigned to words and subwords—are the basic units of data that AI tools process to generate outputs that mimic language without understanding the text itself.

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.

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.

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.

Studies show AI tools can result in passive learning with less retention

Although ChatGPT, Google's AI overviews, and other similar software can save time, a study of more than 10,000 adults showed that reliance on them yielded work products that were more generic and included fewer facts than those produced solely by Google search. Weaker brain connectivity has also been observed when users write using AI.

LLMs are the backbone of AI tools that process and generate natural language

Large language models identify patterns in massive datasets of books, code, and unlabeled text from which to generate coherent responses. GPT-3 was trained on about 45 terabytes of data and uses 175 billion parameters—each parameter being a value the model adjusts as it learns.

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.

Scientists increasingly can't predict what AI will do

Most AI systems are so-called "black box" models, where we only see inputs and outputs while the inner workings remain obscure. This piece, written prior to the launch of ChatGPT, cautions developers to know why and how a system produces its results, and not to sacrifice explainability for accuracy.

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.

Microsoft has integrated AI across its software products with Copilot

It utilizes an underlying model from OpenAI—makers of ChatGPT—to assist users in creating documents, writing code, locating files, and more across Windows, Microsoft 365, Bing search and WhatsApp on desktop and mobile devices. Copilot Voice and Vision offer accessibility features for users who prefer not to use mouse/keyboard navigation.

OpenAI initially focused on novelty projects, like training AI bots to win video games, before becoming known for its AI chatbot ChatGPT.

Artificial general intelligence equals or surpasses human intelligence

AGI incorporates sensory perception, memory, and advanced logical inference systems to move beyond the narrow tasks seen in large language models and chatbots. One of the most significant hurdles in developing AGI is designing systems that learn and can flexibly apply learning across domains.

Modular data centers can be transported on a truck for rapid deployment

Bypassing the complications involved in building larger facilities over the course of several years, such as construction costs and permits, these pre-fabricated, self-contained pods, roughly the size of shipping containers, can carry thousands of graphics processing units for small- and medium-scale AI-related tasks.

As of 2025, ChatGPT accounts for more than half of all chatbot traffic

Since ChatGPT's debut of its chatbot in November 2022, the daily use of generative AI has skyrocketed. This chart shows which chatbots are the most used around globally, from OpenAI's ChatGPT to Perplexity and DeepSeek.

The next technology epoch is here

From ChatGPT to DALL-E, 2022 saw the mainstream emergence of artificial intelligence for consumer use. Here's how these platforms will disrupt today's five biggest tech companies—Apple, Amazon, Meta, Google, and Microsoft.

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.

Stephen Wolfram on AI's rapid progress & the "Post-Knowledge Work" era

AI, as a concept, has been around since at least the 1950s, but it's only in the last year or so that the technology has been able to equal humans at many tasks -- and in many cases surpass humans. In this podcast, mathematician and computer scientist Stephen Wolfram provides an AI 101, explaining how we got here, how highly advanced chatbots like ChatGPT work, and what our world is in store for next.

As of 2025, artificial intelligence systems remain narrow and specialized

Efforts are ongoing to create artificial general intelligence, where machines outperform humans across a wide range of cognitive tasks, including those for which they have not been specifically trained. Current models utilize machine learning and neural networks to perform tasks for which they are programmed, leveraging provided training data.