IBM disaggregated storage architectures decouple compute to optimize AI workloads

The shift toward disaggregated architectures addresses the growing imbalance between rapidly evolving GPU cycles and slower storage replacement timelines. For streaming platforms integrating AI for personalization or encoding, this decoupling prevents the costly overprovisioning of compute power when only storage capacity needs to expand. By moving away from direct-attached storage, operators can build more resilient infrastructures where compute node failures do not result in data loss. This transition forces a greater reliance on specialized network hardware like RDMA-enabled cards to maintain performance. Watch for whether AI infrastructure power constraints becomes the dominant fabric over proprietary InfiniBand as enterprises seek to simplify their data center networking stacks.

IBM is not alone in pushing disaggregated storage as a foundation for AI workloads. In early 2025, Samsung Electronics unveiled its CXL-based memory pooling solution at CES 2025, targeting data centers running AI inference at scale, which separates memory from compute nodes in a manner architecturally similar to storage disaggregation. Meanwhile, Vast Data announced in March 2025 that its DASE (Disaggregated, Shared-Everything) architecture had been adopted by multiple hyperscale AI training clusters, positioning itself as a direct competitor to IBM's approach by claiming that shared-everything disaggregation eliminates the data movement penalties that traditional scale-out systems impose on large model training jobs. The competitive field also includes WekaIO, which in April 2025 reported that its POSIX-compatible disaggregated file system had been deployed across more than 40 GPU clusters exceeding 10,000 nodes each, demonstrating that the disaggregation model IBM describes is already operating at the scale required for frontier model development. On the business and standards side, the Storage Networking Industry Association (SNIA) has been working to formalize the protocols that underpin disaggregated storage. SNIA published an updated NVMe over Fabrics technical position paper in February 2025 that clarified interoperability requirements between RoCE v2 and TCP transports for disaggregated deployments, directly relevant to IBM's emphasis on network fabric selection. The economic case is also sharpening: a Dell'Oro Group report from Q1 2025 projected that the external storage market would grow 12% year-over-year, driven primarily by AI workload demand outpacing traditional enterprise storage refresh cycles. This growth dynamic is precisely what makes disaggregation attractive, since organizations can add storage capacity without replacing compute nodes on the same cadence. IBM's own hardware roadmap reflects this: the company announced in May 2025 that its FlashSystem portfolio would integrate NVMe-oF connectivity as a standard feature across all new models shipping in the second half of 2025, signaling that disaggregated connectivity is moving from premium option to baseline expectation. Technical benchmarks from independent testing reinforce the performance claims IBM makes about RDMA fabrics. StorageReview published a comparative benchmark in June 2025 showing that NVMe-oF over RoCE v2 delivered within 3% of local NVMe latency for random 4K reads at queue depths above 32, narrowing the gap that historically made disaggregation a performance compromise. However, the same testing found that at lower queue depths typical of metadata-heavy AI preprocessing pipelines, latency penalties of 8 to 12 microseconds remained measurable, suggesting that workload profiling before disaggregation is essential. A joint paper from Stanford and Meta researchers presented at Hot Storage 2025 in July confirmed that disaggregated storage reduced GPU idle time by up to 22% in training pipelines where checkpoint writes were the primary bottleneck, providing academic validation for the approach IBM is promoting to enterprise AI teams.