Organizations running generative AI and machine learning workloads on Amazon Elastic Kubernetes Service (EKS) need effective caching strategies to optimize both performance and costs. Container image caching is essential to reduce image pull latency, allowing workloads to start processing data quickly. Options like using data volumes in Bottlerocket AMIs and Amazon Elastic Block Store (EBS) volumes on Amazon Linux 2023 can significantly cut down image pull times. Bottlerocket’s smaller resource footprint and faster boot times help in minimizing costs by using fewer compute and storage resources. For optimal EBS performance, using EBS-optimized instance types ensures that gp3 volumes maintain 90% of their provisioned IOPS performance almost all the time. For machine learning workloads, the alignment of storage performance with compute resources is critical. Bottlenecks in data loading can lead to longer training times and higher expenses. Key factors affecting performance include dataset size, file count, and access patterns. Low-latency storage options, such as Amazon FSx for Lustre and S3 Express One Zone, are recommended for distributed training workloads. These solutions help maintain performance while also enabling cost savings through strategies like prompt caching and batch processing. Storage systems for machine learning must persist beyond the lifespan of individual pods, ensure availability across all nodes, and provide high durability. AWS storage services are built to support these needs with redundancy to prevent data loss. For instance, Amazon S3 offers scalable and cost-effective storage, while S3 Express One Zone is designed for latency-sensitive applications with single-digit millisecond access times. FSx for Lustre excels in high-performance computing scenarios, delivering massive throughput and IOPS. It also supports lazy loading, where files are cached on first access, allowing for faster subsequent reads. These tailored storage options are crucial for building efficient and robust machine learning infrastructures.