NVIDIA is a technology company known for creating GPUs, the processors that have become the foundation for training and running AI models. Almost every AI lab uses its hardware or software.

How it works

The GPU was born for game graphics, but it has thousands of cores that do calculations in parallel. Training a neural network requires multiplying huge matrices, and that is exactly the kind of calculation where the GPU performs well.

NVIDIA's AI business has three layers:

  • Hardware: cards and accelerators for data centers, like the H100 and B200 lines, plus cards for personal computers.
  • Software: CUDA, the platform that allows programming GPUs, and optimized libraries for AI.
  • Services: NVIDIA NIM offers packaged models to run ready to use.

CUDA is a major differentiator, because many AI tools were written for it. This creates an ecosystem that is hard to replace.

Practical example

You want to run an open model on your computer. If it has an NVIDIA card, tools like Ollama or llama.cpp usually take advantage of the GPU to generate text much faster than with just the processor.

To see if the system recognizes the card:

Terminal
nvidia-smi

The command shows the card model, usage and available video memory. This memory, VRAM, defines the model size that fits on the card.

What it's for

For developers, NVIDIA matters because it defines what you can run locally and how much it costs to train or serve models in the cloud. The price and availability of GPUs directly influence the price of AI APIs.

For those following business, the company is one of the central names in the AI infrastructure market, alongside cloud providers and model labs.

For those building clusters, it is the reference for accelerators, high speed networks and GPU orchestration software.

Caveats

NVIDIA is not the only option. There are accelerators from other companies and cloud chips, like Google's TPUs. For local use, some tools also work with other cards, to varying degrees of support.

Finally, market data and performance change quickly. Check official company sources before using numbers in purchasing or investment decisions.