GPUs & the NVIDIA Ecosystem

Graphics Processing Units were originally designed for rendering video game visuals, but their architecture - thousands of small cores working in parallel - turned out to be ideal for the matrix mathematics that underpin neural networks. NVIDIA recognised this opportunity early and built an ecosystem around it. Their CUDA programming platform, released in 2006, became the standard way to write GPU-accelerated code, and their hardware has dominated AI training and inference ever since. As of early 2026, NVIDIA's data centre GPUs - the H100, H200, and the newer Blackwell architecture - are the workhorses of the AI industry. The company's market capitalisation has grown enormously on the back of AI demand, and their chips are so sought after that major cloud providers and AI labs have placed orders worth billions. NVIDIA's dominance isn't just about hardware performance - it's about the software ecosystem. CUDA, cuDNN, TensorRT, and a vast library of optimised tools mean that switching to a competing chip often requires significant re-engineering. This lock-in is a source of frustration for customers and an opportunity for competitors, but so far nobody has displaced NVIDIA's position in high-end AI training. The company continues to push performance boundaries while gradually facing more credible competition.