"The CUDA toolkit is now packaged with Rocky Linux, SUSE Linux, and Ubuntu. This will make life easier for AI developers on these Linux distros. It will also speed up AI development and deployments on Nvidia hardware. AI developers use popular frameworks like , , and to work on their projects. All these frameworks, in turn, rely on Nvidia's CUDA AI toolkit and libraries for high-performance AI training and inference on Nvidia GPUs."
"Don't know CUDA? It's a parallel computing platform and programming model that enables software developers to use Nvidia GPUs for general-purpose processing instead of graphics rendering. By leveraging thousands of GPU cores, CUDA enables massive parallelism, speeding up complex computations in fields like AI, scientific computing, machine learning, and data analysis. CUDA also provides application programming interfaces (APIs) and libraries for C, C++, Python, and other languages."
"The CUDA Toolkit will now be incorporated in the official package feeds of SUSE Linux, Ubuntu, and . This native packaging marks a significant step in democratizing GPU acceleration, enabling developers and enterprises to deploy complex, GPU-hungry applications faster and with less risk of installation or configuration mismatches. This will also enable streamlined deployment of CUDA, which can reduce time-to-production from weeks to minutes, thus drastically shortening deployment cycles for AI workloads."
CUDA is a parallel computing platform and programming model that lets developers use Nvidia GPUs for general-purpose processing, enabling massive parallelism across thousands of GPU cores. Nvidia partnered with enterprise Linux distributors Rocky Linux, SUSE Linux, and Ubuntu to package the CUDA Toolkit into their official package feeds. Native packaging reduces installation and configuration mismatches, synchronizes package naming with Nvidia standards, and speeds package updates after Nvidia releases. The move streamlines deploying GPU-accelerated frameworks and can reduce time-to-production from weeks to minutes, accelerating AI training, inference, and enterprise deployments on Nvidia hardware.
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