What Is a GPU and Why Does AI Need So Many of Them?

GPUs are the engine of the AI revolution. Nvidia, the company that makes most of them, has become one of the most valuable companies in the world because of it. But what actually is a GPU, and why does AI need them? Here is the explanation that does not require a computer science degree.

What is a GPU?

GPU stands for Graphics Processing Unit. It was originally designed to do one thing: render graphics for video games and visual applications. Drawing a 3D scene on a screen requires performing millions of tiny mathematical calculations simultaneously — calculating the colour of each pixel, the angle of light, the position of every object.

A regular CPU (Central Processing Unit — the "brain" of your computer) handles tasks one at a time, very quickly. It is excellent at complex, sequential logic. But rendering graphics requires doing millions of simple calculations at the same time, which is exactly what a GPU is built for.

The key difference: parallel processing

A CPU has a small number of powerful cores (typically 4 to 16). It is like having a few expert mathematicians who can solve complex problems one at a time, very quickly.

A GPU has thousands of smaller cores (modern AI GPUs have over 16,000). It is like having an army of calculators, each handling a simple problem, all working simultaneously.

This parallel architecture is what makes GPUs perfect for AI.

Why does AI need GPUs?

AI models — the kind that power ChatGPT, Claude, Gemini, and the agents businesses are deploying — are built on neural networks. A neural network is essentially a massive web of mathematical connections (called parameters) that the model uses to process information.

GPT-4, for example, is estimated to have over 1 trillion parameters. When you send a message to an AI and it generates a response, the model is performing calculations across billions of those parameters simultaneously. This is a massively parallel workload — exactly the kind of task GPUs excel at.

Training vs inference

GPUs are used for two distinct AI tasks:

Training is the process of building an AI model. It involves feeding the model enormous amounts of data and adjusting its parameters over weeks or months until it learns to generate useful outputs. Training GPT-4 reportedly required thousands of GPUs running for months at a cost exceeding $100 million.

Inference is the process of using a trained model — every time you send a message to ChatGPT or your AI agent answers a phone call, that is inference. Inference requires fewer GPUs than training, but at scale (millions of users making millions of requests), the demand is enormous.

Why Nvidia dominates

Nvidia manufactures roughly 80 to 90 percent of the GPUs used in AI data centres worldwide. Their dominance comes from a combination of hardware and software:

The hardware — Nvidia's AI GPUs (the H100, H200, and now the Blackwell B200) are purpose-built for AI workloads with specialised cores for the types of math neural networks require

CUDA — Nvidia's software platform that lets developers write programs for GPUs. CUDA has been around since 2006 and has become the standard. Almost all AI software is written to run on CUDA, which creates a massive ecosystem advantage

The financial results — Nvidia's data centre division generated **$62.3 billion in a single quarter**, making it one of the most profitable technology businesses in history. Gaming GPUs, by comparison, generated just $3.7 billion.

The company's market capitalisation has at times exceeded $3 trillion, making it one of the most valuable companies on Earth — all because AI cannot function without its products.

The GPU shortage

There are not enough GPUs to meet demand. According to Clarifai's analysis, data centre GPUs are effectively sold out, with lead times stretching to 36 to 52 weeks. If you order an AI GPU today, you might not receive it for a year.

The reasons:

Explosive AI demand — every major technology company, and thousands of startups, need GPUs

Manufacturing constraints — the most advanced GPUs are manufactured by TSMC in Taiwan, which has limited capacity

Memory shortages — as covered in our article on RAM, the specialised memory AI GPUs need (HBM) is sold out globally, constraining how many GPUs can be produced

Nvidia's prioritisation — Nvidia is allocating its limited supply to data centre customers (who pay more) over consumer gaming customers

The competition

Other companies are trying to break Nvidia's dominance:

AMD — makes competing GPUs (the Instinct MI series) with growing AI capabilities but a much smaller market share

Google — designs its own TPU (Tensor Processing Unit) chips, used internally for Google's AI services

Amazon — developed Trainium and Inferentia chips for its AWS cloud

Apple — the M-series chips in Mac computers have integrated GPU cores that can run smaller AI models locally

Meta, Microsoft, and others are developing custom silicon

Despite these efforts, Nvidia's ecosystem advantage means it will likely dominate AI computing for years to come.

What does this mean for business owners?

1. AI is not free — and the reason is hardware. When you pay for API tokens to run your AI agent, a meaningful portion of that cost goes toward the GPU infrastructure powering the model. Understanding this helps you appreciate why AI services cost what they do — and why those costs have been falling as GPU efficiency improves.

2. Local deployment is an option. Modern Mac computers with Apple Silicon (M4 and M4 Pro chips) have integrated GPU cores capable of running AI models locally. This is why we deploy AI agents on Mac Minis — the hardware can handle the workload for a single business without needing a data centre GPU.

3. The cost trajectory is favourable. Despite the shortage, the cost of AI inference has dropped roughly 75 to 80 percent over the past two years. Each new generation of GPU is more efficient than the last. For businesses, this means the ongoing cost of running AI agents will continue to fall even as capabilities improve.

The GPU is the fundamental building block of modern AI. Understanding what it is and why it matters gives you better context for every AI decision your business makes.

If you have questions about the hardware behind your AI agent, we explain it in terms that make sense.