What is NVIDIA? GPUs, CUDA, and AI computing explained

NVIDIA Corporation is an American technology company that designs graphics processing units (GPUs), other processors, networking hardware, and software. Its products are used in gaming, artificial intelligence, scientific computing, professional graphics, and robotics. NVIDIA is headquartered in Santa Clara, California.

The company is best known for its GPUs, but its role extends from individual chips to complete computing systems and the tools developers use to program them. Understanding those different layers helps explain why the NVIDIA name appears on a gaming PC, an AI server, and a cloud computing service.

What does NVIDIA actually do?

NVIDIA develops computing technology and sells hardware, software, and related services. Some customers buy graphics cards or integrated systems. Others encounter NVIDIA hardware inside a computer made by another company or rent access to it through a cloud provider.

Three layers are useful to distinguish:

• Processors and networking: GPUs, CPUs, network adapters, switches, and related components perform calculations and move data.
• Systems: products such as DGX combine computing components into machines or larger infrastructure for AI workloads.
• Software: drivers, CUDA, libraries, and development tools let applications use the hardware.

These layers can be sold or accessed in different ways. Buying a GPU does not automatically include every enterprise software license or a managed AI service. Renting a server with NVIDIA GPUs does not mean NVIDIA operates the service or sets its billing terms.

How NVIDIA GPUs work

A graphics processing unit is designed to perform large amounts of parallel work. Computer graphics provided an early application: rendering an image involves many operations on pixels, geometry, and lighting that can be divided across processing resources.

Similar patterns appear in other fields. A neural network performs repeated operations on arrays of numbers. A scientific simulation may update many points in a grid. GPU technology can accelerate those tasks when the software organizes them for parallel execution and keeps the processor supplied with data.

A GPU normally works alongside a CPU, or central processing unit. The CPU runs the operating system, coordinates the application, and handles work that is less suitable for the accelerator. Moving data between components also takes time, so a GPU does not make every program faster. Our introduction to GPU computing explains when acceleration helps and where its limits arise.

What is CUDA, and why does it matter?

CUDA is NVIDIA's parallel computing platform. Its name originated as Compute Unified Device Architecture. NVIDIA introduced the architecture in 2006 to make GPU processing available for workloads beyond graphics, as described in the CUDA programming guide.

The CUDA Toolkit provides development components such as a compiler, libraries, a runtime, and debugging tools. Developers can write GPU code themselves or use applications and machine learning frameworks that call accelerated libraries on their behalf.

This distinction matters for everyday use. Someone running a compatible AI application may not need to write a CUDA kernel, but the application still needs a supported GPU and a compatible software environment. A driver, a toolkit, and a framework package have different roles; installing one does not establish that every application will work.

For example, a researcher can use a framework to train an image classifier while optimized GPU libraries perform much of the numerical work. The researcher still needs to check memory requirements and supported versions. CUDA support is a compatibility requirement to verify, not a promise of a particular speedup.

What does NVIDIA compute capability mean?

A GPU's compute capability identifies the hardware features and instructions its architecture supports. This number is separate from the CUDA Toolkit version, CUDA core count, and memory capacity. Look up the exact GPU, then check the architectures supported by the application and software release you intend to use. Compute capability helps establish compatibility; it does not tell you how fast a complete job will run.

What is NVIDIA Nsight Compute?

NVIDIA Nsight Compute is a developer tool for profiling GPU kernels, the functions that execute on the GPU. Its graphical interface and command-line interface collect detailed metrics that help explain how a kernel uses computing resources and GPU memory. You do not need to run the profiler simply to use a compatible CUDA application.

Nsight Systems examines activity across the application, including CPU work, GPU work, and transfers. That broader view can help locate a bottleneck before you investigate an individual kernel with Nsight Compute. For example, a job that spends most of its time waiting for input needs a different investigation from one dominated by a slow GPU kernel.

Profiling changes execution. NVIDIA's profiling guide explains that collecting metrics can require replaying kernels and saving or restoring memory, which adds overhead. Start with a small set of relevant metrics, and measure normal end-to-end runtime separately. A longer run under the profiler does not, by itself, establish that the application became slower.

NVIDIA products and their uses

GeForce: gaming graphics and creative work

GeForce is NVIDIA's consumer graphics product family. GeForce RTX GPUs support features for rendering, ray tracing, and AI-assisted graphics. NVIDIA's DLSS technologies include reconstructing higher-resolution images from lower-resolution input and, on supported hardware, generating additional frames. Available features depend on the GPU generation, application, and settings.

A GeForce card can also run compatible compute workloads. Its usefulness for an AI model depends on memory, software support, and the task, rather than its gaming label alone. Consumer and data-center products are different configurations, even when both can execute CUDA applications.

GeForce NOW is a separate cloud gaming service. It streams supported games from remote systems. A cloud gaming subscription is not equivalent to renting a general-purpose GPU instance for your own training code.

RTX PRO: professional visualization

NVIDIA RTX PRO products address professional work such as engineering design, rendering, simulation, and AI development. NVIDIA Quadro is an older professional graphics brand that readers may still see on existing machines or in older documentation.

For professional software, check the application's supported hardware and drivers. A product with a higher model number does not automatically provide the right memory capacity, certifications, or deployment features for a particular workflow.

Data-center GPUs, CPUs, and DGX systems

Data-center offerings serve workloads such as AI training, inference, data analysis, and high performance computing. Names including A100, H100, H200, and B200 identify GPU products or families with different memory, architecture, and system requirements. They are not interchangeable labels for a single type of AI server.

NVIDIA also designs CPUs. The Grace CPU, for example, uses the Arm architecture and is designed for data-center computing. A CPU and a GPU remain different processor types, even when they are tightly connected within a system.

DGX refers to integrated AI computing systems rather than one GPU model. Its range covers different scales and configurations. Our NVIDIA DGX guide explains how systems, software, and deployment options fit together.

Networking for connected computing

Large AI jobs may divide work across several GPUs or servers. Those devices must exchange data, so networking can affect how much useful performance the system delivers. NVIDIA supplies networking technology including InfiniBand and Ethernet products.

NVIDIA completed its Mellanox acquisition in April 2020, following the deal's announcement in 2019. That distinction matters when reading older company timelines. The acquisition expanded NVIDIA's networking capabilities alongside its compute products.

Robotics, vehicles, and simulation

Jetson modules and developer kits support edge AI, where computation happens near a device or sensor. NVIDIA Isaac provides robotics development tools, including simulation and robot-learning resources.

For vehicles, NVIDIA DRIVE supplies computing and software technology used in assisted and automated driving development. NVIDIA provides technology to vehicle developers; a vehicle containing NVIDIA hardware is not automatically self-driving.

NVIDIA Omniverse offers libraries and development tools for applications involving 3D worlds and simulation. Such tools can help developers construct and test virtual environments. The accuracy of a simulation still depends on its models, inputs, and validation against the real system.

Why is NVIDIA associated with artificial intelligence?

Deep learning uses numerical operations that GPUs can process in parallel. Training adjusts a model's parameters from examples; inference uses the trained model to produce outputs. Both can benefit from acceleration, although their memory, latency, and throughput requirements differ.

NVIDIA's role combines GPU hardware with software that makes the hardware usable in applications. Libraries, framework support, profiling tools, and deployment software matter because a chip's theoretical performance is useful only when the application can use it.

That does not make NVIDIA synonymous with AI. AI models are developed by many organizations, and accelerated applications can use other hardware and software stacks. Nor does an “AI chip” label guarantee that a particular model will fit in memory or meet a response-time target.

For a practical deployment, ask what the application supports, what memory it needs, and what a representative test shows. Compare the complete job, including data loading and output quality, rather than treating an advertised compute figure as the result you will obtain.

Does NVIDIA manufacture its own chips?

NVIDIA designs chips and uses manufacturing partners to produce them. Its fiscal 2026 annual filing describes a fabless and contract manufacturing strategy, including foundries such as Taiwan Semiconductor Manufacturing Company (TSMC) and Samsung.

“Fabless” means the company relies on external semiconductor fabrication rather than operating the factories that produce its wafers. Manufacturing also involves packaging, assembly, and testing. The NVIDIA name identifies the chip designer; it does not mean every production step happens inside an NVIDIA-owned facility.

A short history of NVIDIA

Jensen Huang, Chris Malachowsky, and Curtis Priem founded NVIDIA in 1993. Its early focus was 3D graphics for gaming and multimedia. NVIDIA's company timeline identifies CUDA in 2006 and the GPU-supported AlexNet breakthrough in 2012 as milestones in its expansion into general-purpose computing and AI.

The 2020 Mellanox acquisition broadened its data-center networking business. Its present product range reflects that expansion: graphics, general-purpose acceleration, networking, and software are connected parts of the company. This history explains the range more clearly than a changing stock-market valuation.

Using NVIDIA hardware through cloud computing

You can use NVIDIA GPUs without buying a local machine by renting capacity from a provider. The provider determines the instance configuration, access method, availability, and service terms. A GPU model name alone does not tell you whether a service includes storage, a supported software environment, or managed operations.

Our NVIDIA cloud computing guide separates rented GPU instances from software catalogs and other services. Before launching a workload, check:

• The exact GPU model, available memory, and allocation method.
• Support for your operating system, drivers, framework, and required libraries.
• The CPU, host memory, storage, and network resources supplied with the instance.
• Pricing, storage persistence, shutdown behavior, and the resources that remain billable.
• Performance on your own workload, including setup time and the required output quality.

Compute with Hivenet is one way to access NVIDIA GPU compute. Check the current offering and selected configuration before launching; a general NVIDIA product list should never be read as a provider's inventory.

Frequently asked questions

Is NVIDIA a GPU?

No. NVIDIA is the company. A GPU is a type of processor, and NVIDIA designs multiple GPU families alongside CPUs, networking products, systems, and software.

Is NVIDIA an American company?

Yes. NVIDIA is headquartered in Santa Clara, California. It works with manufacturing partners and customers internationally.

Does every AI application need an NVIDIA GPU?

No. Some applications run on CPUs or other accelerators. The appropriate hardware depends on software support, model size, performance requirements, and cost.

Is CUDA the same as a graphics card driver?

No. CUDA is a computing platform; the CUDA Toolkit contains development tools and libraries. A graphics driver is another part of the software environment. An application may need specific compatible versions, so follow its installation requirements.