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GPU Computing
GPU Computing Facts
Why Choose Tesla
Tesla GPU Accelerators
High Performance Computing

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, engineering, and enterprise applications. Pioneered in 2007 by NVIDIA, GPUs now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world.

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU consists of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

Tesla Workstation Cards Memory Max. Power Peak double precision floating point performance
Peak single precision floating point performance
NVIDIA Tesla K40 Workstation Card by PNY 12GB GDDR5 235W 1.43 Tflops 4.29 Tflops
NVIDIA Tesla K20 Workstation Card by PNY 5GB GDDR5 225W 1.17 teraflops 3.52 teraflops

Find out which GPU is best for your application on PNY's online configurator:

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