AWS Optimizes Storage For EKS AI Workloads
Amazon Web Services details storage and caching strategies for generative AI and ML workloads on Amazon EKS, covering container image caching, model checkpointing, and inferencing performance. The post compares options including Bottlerocket, EBS gp3 with EBS-optimized instances, Amazon S3, S3 Express One Zone, and FSx for Lustre, and quantifies impacts like 90% gp3 IOPS delivery and 5,500 S3 GETs/sec. Practitioners should align storage with compute to reduce latency and costs.
Key Points
- 1Describes container image and model caching options like Bottlerocket, EBS gp3, S3, FSx.
- 2Explains how storage latency and throughput affect GPU utilization, training time, and operational costs.
- 3Recommends aligning storage with compute, using EBS-optimized instances and low-latency stores for performance.
Scoring Rationale
Actionable, industry-wide AWS guidance with measurable metrics and deployment advice, limited novelty because it summarizes vendor best-practices rather than introducing new technology.
Sources
Public references used for this report.
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