The white paper Optimizing Cloud Performance for AI Workloads explains how enterprises can maximize ROI by aligning cloud infrastructure choices with the unique demands of AI. While cloud platforms provide elasticity, scalability, and access to advanced tools, they also introduce cost and complexity challenges. The paper offers a framework for optimizing AI workloads through three focus areas: (1) choosing the right VM types, by matching GPU or CPU-optimized instances to specific AI lifecycle stages; (2) optimizing performance and scalability, by designing modular pipelines, using autoscaling and observability tools to prevent bottlenecks and overprovisioning; and (3) balancing operational costs and capabilities, by embedding cost-aware design, using efficient architectures like AMD EPYC CPUs, and continuously monitoring multi-cloud spend. Ultimately, success requires treating cloud AI infrastructure as a dynamic, workload-specific system—balancing agility, performance, and cost for long-term business impact.

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