--- title: "What Is a GPU, and Why Does AI Run on One?" description: "A chip built to draw video-game graphics now sits at the center of the AI boom — and explains why Nvidia became one of the world's most valuable companies. Here's how a GPU works, and why artificial intelligence depends on it." category: "Tech" category_url: https://boursel.com/category/tech author: "Sofia Marchetti" published: 2026-06-26T16:42:00.000Z updated: 2026-06-26T16:42:00.000Z canonical: https://boursel.com/article/what-is-a-gpu-and-why-does-ai-run-on-one tags: ["gpu", "nvidia", "ai-chips", "cuda", "semiconductors"] --- # What Is a GPU, and Why Does AI Run on One? A chip built to draw video-game graphics now sits at the center of the AI boom — and explains why Nvidia became one of the world's most valuable companies. Here's how a GPU works, and why artificial intelligence depends on it. *This is general information, not investment advice.* The hardware powering ChatGPT and every major AI model started life rendering polygons in video games. Here's how that happened. ## A chip born for pixels A **GPU** (graphics processing unit) is a chip designed to do the math that draws images — color, light and texture for millions of pixels at once. To do that, GPU makers packed **thousands of small calculating cores** onto one chip, all working simultaneously. Nvidia shipped its first modern GPU in 1999 for smoother games. Two decades later, that same design turned out to be exactly what AI needed. ## CPU vs. GPU Its counterpart is the **CPU** (central processing unit), the chip in every laptop. A CPU has a few very powerful cores optimized for **sequential** work — running an operating system, a browser, anything with complex, branching logic. A GPU trades that for **throughput**: thousands of weaker cores doing many identical calculations **in parallel**. When a problem splits into thousands of identical sub-tasks, a GPU finishes in a fraction of a CPU's time. Nvidia's data-center H100 chip has roughly **16,896** such cores; benchmarks show 40x-plus speedups over CPUs on the math AI relies on, [as explained by infrastructure providers like Civo](https://www.civo.com/blog/why-gpus-accelerate-ai-learning). ## Why AI is, underneath, matrix math Modern AI — the neural networks behind chatbots and image generators — reduces to one operation done over and over: **matrix multiplication** (multiplying big grids of numbers). Training a model runs this forward and backward across billions of examples to tune billions of "weights." That work is **embarrassingly parallel** — each piece can be computed at the same time — which maps perfectly onto a GPU's thousands of cores. That single architectural fit is why GPUs, not CPUs, became the workhorse of AI. ## CUDA: the real moat Nvidia's hardware lead is real, but its deeper edge is **software**. In 2007 it released **CUDA**, a toolkit that let developers program its GPUs in familiar languages. Over nearly two decades, the entire AI software stack — including the PyTorch and TensorFlow frameworks researchers use daily — grew up on CUDA. Switching to a rival's chip means rewriting thousands of small engineering decisions, so the **lock-in compounds** quietly, [analysts note](https://www.cnbc.com/2025/11/21/nvidia-gpus-google-tpus-aws-trainium-comparing-the-top-ai-chips.html). It's why competitors with comparable hardware still struggle to win share. ## Nvidia's grip — and the price The result is dominance: estimates put Nvidia's share of the AI-accelerator market around **80%+**. A single top AI GPU (the H100) sells for roughly **$25,000–$40,000**, and AI data centers pack **tens of thousands** of them — which is what makes the AI build-out so capital-intensive (Microsoft, Google, Amazon and Meta together guided to **$300 billion-plus** of 2025 capex, the spending now under market scrutiny in the AI selloff). ## The supply chain — and the competition Nvidia *designs* GPUs but doesn't make them: that's **TSMC** in Taiwan (tying GPUs to the chip-concentration risk we've covered), and they need the **high-bandwidth memory** from SK Hynix, Samsung and Micron that we've tracked through the memory squeeze. Rivals are pushing hard: **AMD's** Instinct line, and custom chips from the cloud giants — Google's **TPUs**, Amazon's **Trainium**, Microsoft's **Maia** — designed to be cheaper and more efficient for specific tasks. Custom chips are growing fast, but none has yet dented Nvidia's lead in general-purpose AI training, where CUDA still rules. ## The power bill GPUs are **power-hungry**. At scale, an AI data center can draw hundreds of megawatts — enough for a small city — and global data-center electricity use rose about **17% in 2025**, [per the IEA](https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions), driven largely by AI. For many new sites, the binding constraint isn't buying GPUs but getting a grid connection — the same tension behind our energy-and-data-center coverage. ## What it means A chip designed to shade polygons turned out, by accident of architecture, to be the right tool for the matrix math at the heart of machine intelligence. Nvidia's head start in silicon — and especially software — made it the default infrastructure of the AI economy. Rivals and the cloud giants are spending to break that grip, but for now nearly every major AI model in the world runs on a chip descended from a late-1990s graphics card. ## Sources - [Why GPUs accelerate AI: the power of parallel math](https://www.civo.com/blog/why-gpus-accelerate-ai-learning) - [Nvidia GPUs, Google TPUs, AWS Trainium: comparing the top AI chips](https://www.cnbc.com/2025/11/21/nvidia-gpus-google-tpus-aws-trainium-comparing-the-top-ai-chips.html)