Multi-tier storage emerges as cost lever for AI inference workloads

As AI inference workloads increasingly dominate enterprise infrastructure, the storage architecture challenge has shifted from raw capacity to intelligent tiering. The panel discussion reveals that monolithic storage solutions built for massive scale do not fit the diverse needs of AI agents and KV cache workloads, which require different performance and cost tradeoffs depending on access patterns and lifecycle stage. Intel's contribution through QuickAssist Technology—moving compression and encryption into hardware rather than consuming CPU cycles—reflects a broader shift: storage must now move at compute speed to avoid bottlenecking GPUs.

The multi-tier approach detailed by Scality and others directly addresses GPU utilization, the ultimate cost driver in AI infrastructure. By enabling GPU-direct storage access and S3 over RDMA, enterprises can stream data directly from object storage into GPU memory, eliminating intermediate data copies that waste both time and power. This is especially critical for large-scale deployments handling petabyte to exabyte data volumes, where not all data can or should live in expensive flash. The engineering challenge, as WekaIO's Anthony Lembo emphasized, is operational simplicity—selecting the right storage technology for each workload tier to minimize data movement and reduce the temptation to over-provision GPU capacity.