Graphic processing units rely on a hierarchy of memory for optimization, where the smallest, fastest memory—registers—is located closest to the core and provides access to data in a fraction of a nanosecond.

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.

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.

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.

See how computer memory really works—from binary bits to modern RAM

The video explains how computers store information as 1s and 0s, how RAM functions as fast working memory, and why systems utilize different types of storage. The video demonstrates how this process enables computers to operate.

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.

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

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 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.

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.

Various types of memory, each serving unique functions, create a complex brain system

These forms of memory, which can overlap in daily life, have been sorted into broad categories, such as short-term and long-term memory, and subtypes such as implicit memory, which uses saved information without conscious awareness that it’s being recalled.

Memory exists as multiple types stored across various brain systems

Memory isn't one uniform function—it's made up of multiple systems, like declarative (facts/events) and non-declarative (skills/habits), which involve different brain areas such as the hippocampus, amygdala, and cerebellum.

Short-term memory is a temporary space for limited, newly learned information

Short-term memory stores info for 15 to 30 seconds and is managed by the prefrontal cortex. Though they're often confused, short-term memory stores information, while working memory actively uses it to help you reason, learn, and solve problems, such as a math equation.

The Worldwide LHC Computing Grid—a distributed system of 2 billion gigabytes of storage across over 40 countries—manages data produced by the Large Hadron Collider.

Explore graphics processing unit benchmarks for 2026, led in performance by Nvidia's GeForce RTX 5090, with AMD's Radeon RX 9060 XT 8GB and Nvidia's GeForce RTX 5060 providing the best 1080p performance per dollar.

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

Watch how graphics cards are fixed in a workshop by replacing faulty components, including graphics processing units and memory, located atop printed circuit boards.

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.

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.

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.

When buying a custom computer, selecting integrated graphics is more affordable, but a dedicated graphics card should be considered for CAD, video content creation, high-end gaming, and 3D modeling.

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.

The hippocampus, a structure in the brain, plays a crucial role in memory formation

The seahorse-shaped structure transfers short-term memories to long-term storage during sleep. It is located in the temporal lobes, above each ear, and aids in spatial navigation and remembering directions.

Memory is a complex narrative constructed from real and imagined experiences

Memories start with perception and are processed in the hippocampus. Different types of memories use different brain regions, and episodic memory, which is involved in recalling one's life events, is more susceptible to decay than semantic memory, which is involved in general information.

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

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.

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.

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.

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.

Laser disc technology stores binary data in etched pits and lands

The pits are dips in the LaserDisc, CD, DVD, or Blu-ray surface that do not reflect the laser from the optical drive back to the photoelectric cell, resulting in a coded zero. Lands are reflective plateaus on the surface that code for a one.

Compression algorithms can reduce file sizes without losing quality by consolidating repeated data or replacing blocks of code with more compact representations, thereby reducing storage demands and improving data transmission rates.

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’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.

Lasers help build the layers of circuit components in microchips

During the photolithography process, light is projected through a blueprint onto a light-sensitive chemical layer on silicon wafers, etching patterns that become transistors through a series of successive reactions. These processes require significant electricity, as only about 2% of photons reach the wafer.

Engrams are the specific pattern of brain cells that make up a memory

Memory isn’t stored in your brain in a neat little package but is instead spread across a pattern of cells in different regions. Understanding this process could lead to better treatments for conditions like Alzheimer’s or PTSD.

Sensory memory is information collected from your senses

Lasting seconds before moving into short-term memory or being forgotten, sensory memory aids in environmental awareness and navigation. Key brain regions involved include auditory, somatosensory, gustatory, visual, and olfactory cortices.

LLMs explained simply and quickly

Large language models generate responses by predicting one token at a time, based on patterns they’ve learned from massive datasets. This explainer covers their core components: data, architecture, training, and how transformer models use context to simulate conversation.

Grace Hopper warned against prioritizing hardware purchases in future computing

Foreseeing the rising volume of data and the increasing demand for its processing and access, she advocated that organizations first map the total flow of information they require and then choose infrastructure to support it, rather than wastefully spending on the fastest computing technology and forcing software to compensate for unnecessary hardware or incorrect data.

Large language models struggle to play video games, despite coding them easily

As of 2025, given clear tasks, specific feedback, and test results to learn from, LLMs have been successful at coding simple video games that resemble pre-existing ones. However, given the variety of games, the lack of training datasets, and the need for spatial awareness, which LLMs find challenging, some of these models experience greater difficulty navigating and "winning" games.