Explore your GPU compute capability and learn more about CUDA-enabled desktops, notebooks, workstations, and supercomputers.

The NVIDIA® CUDA® Toolkit provides a development environment for creating high-performance, GPU-accelerated applications. With it, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms, and supercomputers.

CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.

2017年1月25日 · A quick and easy introduction to CUDA programming for GPUs. This post dives into CUDA C++ with a simple, step-by-step parallel programming example.

Get the latest feature updates to NVIDIA's compute stack, including compatibility support for NVIDIA Open GPU Kernel Modules and lazy loading support.

Q: Which GPUs support running CUDA-accelerated applications? CUDA is a standard feature in all NVIDIA GeForce, Quadro, and Tesla GPUs as well as NVIDIA GRID solutions. A full list can be found on the CUDA GPUs Page.

Since its introduction in 2006, CUDA has been widely deployed through thousands of applications and published research papers, and supported by an installed base of over 500 million CUDA-enabled GPUs in notebooks, workstations, compute clusters and supercomputers.

A collection of GPU-accelerated libraries, tools, and technologies that deliver higher performance than CPU-only alternatives, across multiple application domains, from AI to HPC.

Find the compute capability of the latest CUDA Capable NVIDIA GPUs. Click on sections below to expand

Install the free CUDA Toolkit on a Linux, Mac or Windows system with one or more CUDA-capable GPUs. Follow the instructions in the CUDA Quick Start Guide to get up and running quickly. Or, watch the short video below and follow along.