THE EVOLUTION OF GPU INFRASTRUCTURE: BEST PRACTICES FOR ENTERPRISE AI DEPLOYMENTS

Abstract

This article presents a comprehensive framework for optimizing and managing Nvidia GPUs in large-scale AI workloads, with a particular focus on large language model deployment and training. It addresses critical challenges in GPU resource management, memory optimization, and infrastructure automation while presenting novel approaches to scaling AI workloads in production environments. It explores advanced techniques in model architecture optimization, including various parallelism strategies and quantization methods, alongside practical solutions for monitoring and maintaining GPU clusters. It demonstrates a significant improvement in resource utilization and operational efficiency by implementing automated management systems and optimized data pipelines. This article also introduces best practices for DevOps integration and infrastructure scaling, providing valuable insights for organizations deploying GPU-accelerated AI systems. These articles have been validated across multiple production environments, offering a robust foundation for future developments in GPU-accelerated AI infrastructure management.