Edge Computing and AI: Extending Cloud Capabilities with NVIDIA and Kubernetes

Abstract

The rapid growth of the Internet of Things (IoT) and the need for near real-time processing have propelled the adoption of edge computing in a wide range of industries. Meanwhile, artificial intelligence (AI) workloads have increasingly become more demanding, requiring powerful resources and efficient orchestration platforms. This white paper presents a deep-dive exploration into how edge computing, AI, and container orchestration—specifically leveraging NVIDIA technology and Kubernetes—can converge to extend cloud capabilities closer to the source of data generation. We propose a robust architectural design, discuss key methodologies, and highlight implementation details while examining the primary challenges and potential solutions for deploying AI at the network edge. Furthermore, this paper presents use cases and case studies to illustrate the real-world impact of this approach. The paper provides a comprehensive, step-by-step technical roadmap, complete with diagrams, references to relevant tools and frameworks, and guidelines for overcoming common hurdles in building and managing edge AI deployments with NVIDIA hardware and Kubernetes.