GPU (Graphics Processing Unit)

A GPU, or Graphics Processing Unit, is a specialised computer chip originally designed to render images and video for games and graphics applications. It turns out that the same kind of parallel processing that makes GPUs excellent at rendering pixels also makes them ideal for the mathematical operations that power AI. Today, GPUs are the backbone of the entire AI industry.

Why GPUs matter for AI

Traditional CPUs (the main processor in your computer) are designed to handle one complex task at a time, very quickly. GPUs are designed to handle thousands of simple tasks simultaneously. This is called parallel processing.

AI training and inference involve enormous numbers of matrix multiplications — mathematical operations that can be broken into thousands of independent calculations. A GPU can perform these calculations in parallel, making AI workloads run 10-100x faster than on a CPU alone.

The GPU landscape

The AI GPU market is dominated by NVIDIA, whose chips power the vast majority of AI training and inference worldwide:

• NVIDIA H100/H200: The current standard for AI training and inference in data centres
• NVIDIA A100: The previous generation, still widely used
• NVIDIA RTX series: Consumer GPUs that can run smaller AI models locally
• AMD MI300: An emerging competitor to NVIDIA's data centre GPUs
• Apple Silicon (M-series): Integrated GPU capabilities that can run smaller models on laptops

GPUs and AI costs

GPU availability and cost are major factors in AI economics:

• Training a frontier model requires thousands of GPUs running for months, costing hundreds of millions of pounds
• A single high-end AI GPU (H100) costs approximately £25,000-40,000
• AI companies invest billions in GPU clusters — entire data centres filled with GPUs
• GPU scarcity has been a bottleneck for AI development, with waiting lists for the latest chips

This is why AI API pricing exists: instead of buying your own GPUs, you pay a provider (OpenAI, Anthropic, Google) a fraction of a penny per query to use their GPU infrastructure.

Cloud GPU access

You do not need to buy GPUs to use AI. Cloud providers offer GPU access on demand:

• AWS, Google Cloud, Azure: Rent GPU instances by the hour for training or running models
• Specialised providers: Lambda, CoreWeave, and others focus specifically on GPU cloud services
• AI APIs: The simplest approach — use Claude, GPT, or Gemini through their APIs and the provider handles all GPU infrastructure

GPUs and your AI strategy

For most organisations, the GPU question boils down to:

• API access (most common): You never think about GPUs. The AI provider manages everything. Best for most businesses.
• Cloud GPUs: You rent GPU time to run your own models. Useful for custom AI applications or data privacy requirements.
• On-premise GPUs: You buy and manage your own GPU hardware. Only necessary for organisations with strict data sovereignty requirements or very high-volume AI workloads.