Nvidia's GPU leadership drives creation of a 1.4 trillion dollar datacenter infrastructure market over ten years.

We are witnessing the rise of a completely new computing era powered by extreme parallel computing, or EPC—also referred to as accelerated computing. Within the next decade, a trillion-dollar-plus data center business is poised for transformation, with artificial intelligence as the primary accelerant rippling across the entire technology stack. Nvidia Corp. sits in the vanguard of this shift, forging an end-to-end platform integrating hardware, software, systems engineering and a massive ecosystem. While Nvidia has a 10- to 20-year runway to drive this transformation, the market forces at play are much larger than a single player. This new paradigm is about reimagining compute from the ground up: from the chip level to data center equipment, to distributed computing at scale, data and applications stacks and emerging robotics at the edge. Research indicates that the data center market could reach $1.7 trillion by 2035.

Every layer of the technology stack—from compute to storage to networking to the software layers—will be re-architected for AI-driven workloads and extreme parallelism. For more than three decades, x86 architectures dominated computing, but general-purpose processing is now giving way to specialized accelerators, with GPUs at the heart of this change. AI workloads such as large language models, natural language processing, advanced analytics and real-time inference demand massive concurrency. While storage is sometimes overlooked in AI conversations, data is the fuel that drives neural networks, and AI demands advanced, high-performance storage solutions. AI-driven workloads cause massive east-west and north-south traffic within the data center and across networks; in the world of HPC, InfiniBand emerged as the go-to for ultra-low-latency interconnects, and this trend is now permeating hyperscale data centers, with high-performance Ethernet as a dominant standard which will ultimately prove to be the prevailing open network of choice.

Accelerated computing imposes huge demands on operating systems, middleware, libraries, compilers and application frameworks that must be tuned to exploit GPU resources. System-level software must manage concurrency at unprecedented levels, with GPU-aware operating systems rapidly evolving to support ultra-parallel workloads. The data layer is shifting from a historical system of analytics to a real-time engine that supports the creation of real time digital representations of an organization, comprising people, places and things as well as processes. Intelligent applications are emerging that unify and harmonize data, increasingly with real time access to business logic and process knowledge. Single-agent systems are evolving to multi-agent architectures with the ability to learn from the reasoning traces of humans, while applications increasingly can understand human language, inject intelligence across organizations and support automation of workflows and new ways of creating business outcomes, extending into the physical world with digital twins representing businesses in real time.

The market has recognized that semiconductors are the foundation of future AI capabilities, awarding premium multiples to companies that can capture accelerated compute demand. Nvidia's 65% operating margins have enticed investors and competitors to enter the AI chip market in droves. In the semiconductor performance space, the "haves" led by Nvidia, Broadcom and AMD are outperforming, while the "have-nots," in particular Intel, are lagging. This dynamic reflects a fundamental shift in how the technology industry views the accelerated computing era that began in late 2022, coinciding with the initial buzz around ChatGPT.