Multi-tier storage rewrites the economics of AI inference

SiliconANGLE · 2 months ago · 43

Multi-tier storage architectures are being promoted to cut the cost of AI inference and improve performance by combining flash, object storage, and disk capacity tiers across training and inference pipelines. Samsung’s PM1723 drive is cited as delivering up to 28.4 gigabytes per second sequential read throughput and up to 6.6 million random read IOPS. Enterprises respond by redesigning storage tiering around KV-cache access and reducing data movement to better use GPU resources without relying only on more hardware.