Press Release: Storage and AI Infrastructure Bottlenecks

Storage Emerges as a Critical Constraint in AI Infrastructure Scaling

Publication Date: 5 February 2026

In a recent DCD Compute, Storage & Networking podcast, “Is storage AI’s hidden bottleneck?”, industry leaders from Los Alamos National Laboratory and Magnition address this concept. “Storage is not just a bottleneck; it is a design blind spot in modern AI systems”, says Magnition Storage Janitor, Andy Banta. While attention has focused primarily on GPUs and networking, the panel highlighted storage latency, metadata overhead, and access-pattern mismatch as growing sources of unpredictability in both training and inference environments. 

The discussion focused on how AI workloads put stress on storage systems in ways that traditional architectures were not designed to handle. This includes challenges like sparse access patterns, heavy interaction with metadata, and the growing disparity between word-level application access and page-level flash devices. 

Panelists pointed out that techniques traditionally used in high-performance computing (HPC), such as trading input/output operations per second (IOPS) pressure for bandwidth, become ineffective under AI workloads where tail latency and consistency are more important than average performance.

The session also examined the emerging trade-offs between performance optimization and security controls, as well as the changing role of metadata. Security may well be the biggest bottleneck, as it’s lacking in most design decisions.  These factors are collectively reshaping how architects assess storage design choices for large-scale AI infrastructure.

About Magnition

Magnition develops design, modeling, and simulation software used to analyze and optimize distributed and AI-driven systems. The company focuses on enabling engineers and architects to evaluate system behavior using real workloads and detailed architectural models before deployment.