Architecting AI Storage at Scale: Why Tiered and Disaggregated Architectures Matter

As AI models scale and diversify, from text to image, audio, and video, the volume, velocity, and lifecycle of data are growing faster than compute. To keep GPUs fed and costs controlled, architects increasingly need to match the right storage media (HDD vs. Flash) to each stage of the AI Data Cycle, which is driving tiered and disaggregated storage architectures that align performance, capacity, and access patterns to workload needs. We’ll highlight where host-managed SMR fits as a high-density, cost-efficient HDD tier, enabling an “active archive” that retains massive raw and curated datasets while delivering predictable, sequential access for reprocessing, retraining, and governance, while complementary flash/NVMe tiers serve latency-sensitive preparation and training hot sets.