While GPUs have long been a staple in industries like gaming, video editing, CAD, and 3D rendering, their role has evolved dramatically over the years. Originally designed to handle graphics-intensive tasks, GPUs have proven to be powerful tools for a wide range of computationally demanding applications. Today, their ability to perform massive parallel processing has made them indispensable in modern fields such as data science, artificial intelligence and machine learning (AI/ML), robotics, cryptocurrency mining, and scientific computing. This shift was catalysed by the introduction of CUDA (Compute Unified Device Architecture) by NVIDIA in 2007, which unlocked the potential of GPUs for general-purpose computing. As a result, GPUs are no longer just graphics accelerators; they’re now at the heart of cutting-edge innovation across industries.
In this blog post, we will discuss the NVIDIA GPU operator on Kubernetes and how to deploy it on the Kubernetes Cluster.