GPU (Graphics Processing Unit)

The silicon engine powering the AI revolution and modern enterprise compute.

The global GPU market surpassed $95 billion in 2025, driven overwhelmingly by artificial intelligence workloads rather than gaming. For C-suite leaders, GPUs have become a strategic resource — as critical to competitive advantage as access to capital or talent. Companies that fail to secure adequate GPU compute capacity risk being unable to deploy the AI models that are rapidly becoming table stakes across every industry.

What It Is

A Graphics Processing Unit, or GPU, is a specialised processor originally designed to render images, video, and 3D graphics. Unlike a Central Processing Unit (CPU), which handles tasks one at a time with a few powerful cores, a GPU contains thousands of smaller cores that work in parallel. Think of it this way: a CPU is like a fleet of Formula 1 cars — fast, specialised, and handling one task at a time with precision. A GPU is like a fleet of 10,000 delivery vans — individually slower, but capable of moving massive quantities of goods simultaneously.

This parallel processing architecture makes GPUs exceptionally well-suited for the mathematical operations that underpin machine learning and AI. Training a large language model, running real-time recommendation engines, or processing computer vision tasks all require performing millions of simple calculations at once — precisely what GPUs are built to do. In 2026, NVIDIA's data centre GPU revenue alone exceeds $60 billion annually, a figure that underscores how central these chips have become to enterprise strategy.

Modern enterprise GPUs are nothing like the graphics cards found in consumer gaming PCs. Products such as the NVIDIA H100 and its successors, or AMD's Instinct MI300X, are purpose-built for AI workloads, featuring hundreds of gigabytes of high-bandwidth memory and specialised components called Tensor Cores that accelerate the matrix multiplications fundamental to neural networks. A single high-end AI GPU can cost between $25,000 and $40,000, and large AI training runs require thousands of them operating in concert.

Why It Matters for Business

GPU availability and pricing directly impact your organisation's ability to develop, train, and deploy AI systems. In 2024-2025, many enterprises faced GPU shortages that delayed AI projects by six to twelve months. Companies including Meta, Microsoft, and Google have each committed to purchasing over $10 billion worth of GPUs to secure their AI infrastructure. For smaller organisations, the cost of GPU compute — often $2-4 per hour per high-end GPU in the cloud — represents a significant line item that demands careful financial planning.

Beyond cost, GPU strategy affects your speed to market. Organisations that invested early in GPU infrastructure were able to train and deploy generative AI models months ahead of competitors. In industries such as pharmaceuticals, where AI-accelerated drug discovery can shorten research timelines by years, GPU access translates directly into patent advantages and revenue. JPMorgan Chase, for example, dedicated over 10,000 GPUs to its AI initiatives in 2025, enabling fraud detection models that reduced false positives by 40% whilst catching 30% more fraudulent transactions.

The environmental and energy implications are equally significant. GPU-intensive data centres can consume 5-10 times more power than traditional computing facilities. ESG-conscious boards must weigh the carbon footprint of large-scale GPU deployments against the business value they generate. Leading organisations are addressing this by siting GPU facilities near renewable energy sources and adopting liquid cooling technologies that reduce energy consumption by up to 40% compared to air-cooled alternatives.

Enterprise Use Cases

In financial services, Goldman Sachs deployed GPU-accelerated risk modelling systems that reduced complex Monte Carlo simulations from overnight batch jobs to near-real-time calculations, enabling traders to assess portfolio risk in seconds rather than hours. The system processes over 25,000 risk scenarios simultaneously, a task that would be impractical on CPU-based infrastructure. Similarly, Visa uses GPU clusters to score every transaction in real time, processing over 76,000 transactions per second during peak periods.

In healthcare, Mount Sinai Health System in New York built a GPU-powered medical imaging platform that analyses CT scans, MRIs, and X-rays to detect anomalies such as tumours and fractures. The system reduced radiologist reading times by 35% and improved early detection rates for certain cancers by 20%. In manufacturing, BMW uses GPU-driven digital twins to simulate entire production lines, optimising throughput and identifying bottlenecks before physical equipment is even installed.

Retailers such as Walmart leverage GPU compute for demand forecasting and dynamic pricing, processing billions of data points from point-of-sale systems, weather forecasts, and social media trends to optimise inventory across 10,500 stores globally. The result: a 15% reduction in stockouts and a 10% improvement in fresh produce margins through better spoilage prediction.

Vendor Landscape

NVIDIA dominates the enterprise GPU market with approximately 85% market share in data centre AI accelerators. Its CUDA software platform has become the de facto standard for AI development, creating a significant ecosystem lock-in. Key products include the Hopper H100, Blackwell B200, and subsequent architectures. AMD is the primary challenger with its Instinct MI300X, offering competitive performance at lower price points, though with a less mature software ecosystem. Intel has entered the market with its Gaudi accelerators but holds single-digit market share. Cloud providers — AWS (with Inferentia and Trainium), Google (TPUs), and Microsoft (Maia) — are developing custom silicon that reduces their dependency on NVIDIA, though these chips are available only within their respective cloud platforms.

Pricing varies dramatically: on-demand cloud GPU access ranges from $2-4 per hour for mid-range GPUs to $8-12 per hour for top-tier NVIDIA H100 instances. Reserved capacity and long-term commitments can reduce costs by 40-60%. For on-premises deployment, a single NVIDIA H100 SXM module costs approximately $30,000, but a fully configured eight-GPU server with networking and storage can exceed $300,000.

Strategic Considerations

Executives should approach GPU strategy as a capacity planning exercise akin to managing any critical resource. The key questions are: Do you need dedicated GPU infrastructure, or can you rely on cloud-based access? Organisations with steady, predictable AI workloads often benefit from a hybrid model — owning some GPU capacity for baseline needs whilst bursting to the cloud for peak demand. Be wary of over-provisioning; GPU technology evolves rapidly, and a chip purchased today may be significantly outperformed by next-generation hardware within 18 months. Consider whether your AI strategy truly requires owning GPU infrastructure or whether managed AI services from cloud providers can deliver the same business outcomes at lower total cost. Finally, ensure your procurement team understands that GPU supply chains remain constrained — lead times for large orders can extend to six months or more, and long-term supply agreements with vendors are increasingly a competitive differentiator.