Nvidia created the CUDA parallel-computing platform used by many modern AI systems.
Nvidia introduced CUDA in 2006 as a software platform and programming model for using the company’s graphics-processing units for general-purpose computing. It enabled developers to run massively parallel calculations on GPUs rather than relying only on central processing units.
CUDA became especially important for machine learning because neural-network training requires large numbers of mathematical operations that can be performed simultaneously. Nvidia’s software libraries, developer tools, and hardware ecosystem helped make its GPUs widely used in AI research and commercial systems.
CUDA is not a graphics chip and is not the same thing as a programming language in the usual sense. It is an ecosystem that includes a programming model, compiler tools, libraries, and interfaces. This software advantage helped Nvidia expand beyond video-game graphics into data centers and accelerated computing.