Scaling AI Workloads with NVIDIA DGX Cloud and Kubernetes: A Performance Optimization Framework
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Scaling AI Workloads with NVIDIA DGX Cloud and Kubernetes: A Performance Optimization Framework
Santosh Pashikanti
Independent Researcher, USA
Abstract
As artificial intelligence (AI) workloads become increasingly complex and resource-intensive, organizations face challenges in scaling their infrastructure to meet performance demands. NVIDIA DGX Cloud, combined with Kubernetes, provides a scalable, high-performance computing platform for AI workloads. This white paper outlines a detailed framework for optimizing performance when deploying AI workloads on NVIDIA DGX Cloud using Kubernetes. It delves into architectural considerations, workload scheduling, resource management, and performance tuning strategies. Web references are provided at the end for further exploration.
Key words: NVIDIA DGX Cloud, AI Workload Optimization, TensorRT, Kubernetes GPU Operator, Kubernetes Cluster Security, AI Model Deployment