It develops GPUs, CPUs, networking products, software and integrated computing platforms for data centres and other markets.
GPUs handle highly parallel calculations, while CUDA provides software tools that help developers build and run workloads on NVIDIA hardware.
Nvidia’s infrastructure supports AI training and inference across cloud, enterprise and other computing environments, connecting hardware with software and networking.
For most consumers, NVIDIA is still associated with graphics cards. Inside the technology industry, however, its role is much broader. The company supplies processors, networking equipment and software that form a significant part of the infrastructure used to train and run modern AI systems. Its latest financial results underline that shift: for the quarter ended July 26, 2026, NVIDIA reported USD 96.2 billion in revenue, including USD 89 billion from its Data Center business.
Nvidia is a semiconductor and computing company whose products span gaming, professional visualisation, automotive and data centres. Its most important AI-related business is accelerated computing: using specialised processors to handle workloads that conventional CPUs can process less efficiently.
The company designs GPUs, CPUs, networking products and complete computing systems. It also develops the software that allows those components to work together. NVIDIA's 2026 annual filing describes its data-centre offering as a combination of compute, networking, software and services rather than simply a collection of chips.
A GPU, or graphics processing unit, is a processor designed to perform many calculations simultaneously. That characteristic was originally valuable for rendering graphics, but it also fits the mathematical operations used by neural networks.
AI training is the process of adjusting a model using large amounts of data so it can recognise patterns and produce useful outputs. Inference is what happens when the trained model generates an answer, prediction, image or other result for a user.
Large AI models require enormous volumes of calculations during both stages. GPUs can divide many of these operations across thousands of computing cores, making parallel processing a central advantage for AI workloads.NVIDIA says its GPUs underpin workloads ranging from model training and inference to scientific computing and data analytics.
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The hardware is only part of NVIDIA's proposition. Its CUDA platform provides developers with tools for programming NVIDIA GPUs, while CUDA-X adds libraries and other software designed to accelerate specialised workloads.
This software layer matters because AI developers do not build every operation from scratch. Frameworks and libraries can use NVIDIA hardware through established software interfaces, reducing the work involved in moving workloads onto GPUs.
Nvidia says more than half of its engineers work on software, illustrating how central that layer has become to its strategy.
Generative AI shifted computing demand toward large data-centre systems capable of handling enormous models.NVIDIA's Data Center business now combines GPUs with CPUs, high-speed interconnects, networking and software.
Its fiscal 2026 results show the scale of this transition. Data Center revenue reached USD 193.7 billion for the year, up 68% from the previous year. Data-centre networking revenue also grew sharply as systems increasingly required fast connections between processors.
Modern AI systems cannot be built by installing GPUs alone. Processors need memory, networking, storage, cooling and software that can coordinate thousands of components.
Nvidia therefore sells rack-scale platforms that integrate these elements. Its Blackwell architecture and newer Vera Rubin platform combine GPUs and CPUs with networking technologies such as NVLink, InfiniBand and Ethernet. The company says these systems can connect very large numbers of computing nodes so they operate as a single computing environment.
Training a frontier model can involve distributing computations across large clusters. Developers therefore need not only fast processors but also software, networking and tools that allow thousands of processors to work together.
Nvidia's ecosystem is consequently used across cloud providers, AI model developers, enterprises and startups. Its latest regulatory filing says major cloud providers and AI model makers use its data-centre infrastructure, while NVIDIA also works with AI clouds to broaden access to its systems for startups and other customers.
Nvidia's significance to AI comes from the infrastructure layer it serves. It does not need to develop the world's leading chatbot or foundation model to influence the industry. Its processors, networking technologies and software determine how developers build and operate many of those systems.
That makes NVIDIA's business closely connected to the economics of AI: the amount of computing required, the cost of training and inference, and the infrastructure needed to turn experimental models into widely used products. As AI development expands, decisions about chips, software and data-centre architecture increasingly shape how quickly and affordably new AI capabilities can reach the market.
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Nvidia designs GPUs, CPUs, networking equipment and software, supplying computing infrastructure for gaming, data centres, professional visualisation, automotive applications and artificial intelligence.
GPUs perform many calculations simultaneously, making them suitable for the parallel mathematical operations required when training and running increasingly complex AI models.
CUDA is NVIDIA’s parallel computing platform and software ecosystem that enables developers to program NVIDIA GPUs and use specialised libraries for accelerated computing workloads.
Nvidia combines GPUs, CPUs, networking technologies and software into accelerated computing platforms designed for AI training, inference, analytics and other demanding data-centre workloads.
Nvidia provides infrastructure used to develop and deploy AI systems, influencing computing capacity, software compatibility, networking architecture and the resources required to operate AI workloads