CUDA Cores in Video Cards

Speed up graphics-intensive processes with Compute Unified Device Architecture

Compute Unified Device Architecture (CUDA) accelerates GPU computation processes. The technology was developed for graphics processing units by Nvidia.

With CUDA, researchers and software developers can send C, C++, and Fortran code directly to the GPU without using assembly code. This streamlining takes advantage of parallel computing in which thousands of tasks, or threads, are executed simultaneously.

Information on CUDA Cores

Product shot of the GeForce GTX TITAN Z graphics card from Nvidia
Nvidia

You'll see the term CUDA when you're shopping for a Nvidia video card. You'll often see a reference to the number of CUDA cores when looking at the packaging or reading reviews.

CUDA cores are parallel processors similar to a processor in a computer, which may be a dual or quad-core processor. Nvidia GPUs, though, can have several thousand cores. They are responsible for various tasks that allow the number of cores to relate directly to the speed and power of the GPU.

Since CUDA cores are responsible for dealing with all the data that moves through a GPU, the cores handle things like the graphics in video games in situations such as when characters and scenery are loading.

Applications can be built to take advantage of the increased performance offered by CUDA cores. You can see a list of these applications on Nvidia's GPU Applications page.

CUDA cores are similar to AMD's Stream processors; they're just named differently. But, you cannot equate a 300 CUDA Nvidia GPU with a 300 Stream Processor AMD GPU.

Choosing a Video Card With CUDA

The higher number of CUDA cores typically means the video card provides faster performance overall. But, the number of CUDA cores is only one of several things to consider when you choose a video card.

Nvidia offers a range of cards featuring as few as eight CUDA cores, like in the GeForce G100, to as many as 5,760 CUDA cores in the GeForce GTX TITAN Z.

Graphics cards that have Tesla, Fermi, Kepler, Maxwell, or Pascal architecture support CUDA.