NVIDIA CORP

Our Company NVIDIA pioneered accelerated computing to help solve the most challenging computational problems. NVIDIA is now a data center scale AI infrastructure company reshaping all industries. Our technology stack includes the foundational NVIDIA CUDA development platform that runs on all NVIDIA GPUs, as well as hundreds of domain-specific software libraries, frameworks, algorithms, software development kits, or SDKs, and application programming interfaces, or APIs. This deep and broad software stack accelerates the performance and facilitates the deployment of NVIDIA accelerated computing for computationally intensive workloads such as artificial intelligence, or AI, model training and inference, data analytics, scientific computing, robotics, and 3D graphics, with vertical-specific optimizations to address industries ranging from healthcare and telecom to automotive and manufacturing. Introduced with the Blackwell architecture, our data-center-scale offerings feature extreme co-design where the infrastructure's chips, networking, systems, software, and algorithms are holistically architected and optimized to maximize performance and scale. Hundreds of thousands of GPUs can be interconnected to function as a single giant computer. This type of data center architecture and scale is needed for the development and deployment of modern AI and accelerated computing applications. The GPU was initially used to simulate human imagination, enabling the virtual worlds of video games and films. Today, it also simulates human intelligence, enabling a deeper understanding of language, science, and the physical world. Its parallel processing capabilities, supported by tens of thousands of computing cores, are essential for deep learning algorithms. This form of AI, in which software writes itself by learning from large amounts of data, can serve as the brain of computers, robots, and self-driving cars that can perceive, understand and reason about the world. GPU-powered AI solutions are being developed by thousands of enterprises to deliver services and products that would have been immensely difficult or even impossible with traditional coding. Examples include generative AI, which can create new content such as text, code, images, audio, video, molecule structures, and recommendation systems; and agentic AI where systems of AI models work in concert to automatically complete a task. NVIDIA has a platform strategy, bringing together hardware, systems, software, algorithms, libraries, AI models and training data sets, and services to create unique value for the markets we serve. While the computing requirements of these end markets are diverse, we address them with a unified underlying programmable architecture allowing us to support several multi-billion-dollar end markets with the same underlying technology by using a variety of software stacks developed either internally or by third-party developers and partners. The large and growing number of developers and installed base across our platforms strengthens our ecosystem and increases the value of our platform for our customers. Innovation is at our core. We have invested over $76.7 billion in research and development since our inception, yielding inventions that are essential to modern computing. Our invention of the GPU in 1999 sparked the growth of the PC gaming market and redefined computer graphics. With our introduction of CUDA in 2006, we opened the parallel processing capabilities of our GPU to a broad range of compute-intensive applications, paving the way for the emergence of modern AI. In 2012, the AlexNet neural network, trained on NVIDIA GPUs, won the ImageNet computer image recognition competition, marking the "Big Bang" moment of AI. We introduced our first Tensor Core GPU in 2017, built from the ground-up for the new era of AI, and our first autonomous driving system-on-chips, or SoC, in 2018. Our acquisition of Mellanox in 2020 expanded our offerings to include networking, enabled our platforms to be data center scale, and led to the introduction of a new processor class – the data processing unit, or DPU. Over the past 5 years, we have built full software stacks that run on top of our GPUs and CUDA to bring AI to the world's largest industries, including NVIDIA DRIVE stack for autonomous driving, Clara for healthcare, Omniverse for physical AI applications, and NVIDIA AI Enterprise software – essentially an operating system for enterprise AI applications. In 2023, we introduced our first data center CPU, Grace, built for giant-scale AI and high-performance computing, or HPC. In 2024, we launched the NVIDIA Blackwell architecture – connecting 36 Grace CPUs and 72 Blackwell GPUs in a data center scale, liquid-cooled design – for real-time trillion-parameter inference and training. In fiscal year 2026, we launched and scaled the NVIDIA Blackwell Ultra platform, optimized for agentic, reasoning, and physical AI. Building on the architectural breakthro