Nvidia GPU

Nvidia GPUs are graphics processors made by Nvidia, originally built for games and 3D rendering. Today they play a key role in training and running artificial intelligence models, scientific computing and many cloud services. The ecosystem around CUDA and specialised libraries has made Nvidia the leading platform in this space.

Why GPUs matter for AI

Compared to classic CPUs, GPUs are built for highly parallel computation. That property maps perfectly onto the matrix multiplications neural networks rely on. Training jobs that would take weeks on CPUs finish in hours or days on GPUs. Inference, which means running trained models, is also significantly faster on GPUs.

Which models matter in practice

In data centres cards such as the A100, H100, H200 and newer generations, built specifically for AI workloads, dominate. In workstations RTX cards are common, used for image editing, video rendering or smaller AI experiments. Cloud providers offer these cards by the hour, so even small teams without their own hardware can work with large models.

In everyday marketing

In day to day marketing GPUs matter mostly indirectly, because they enable many AI features, from generative images and text to speech recognition. As soon as you use tools backed by large models, a GPU cluster in a data centre almost always sits behind the feature. Cost, energy use and data protection deserve attention when these services are used.