How legacy storage infrastructure could endanger your future.

Everyone knows AI runs on data, but few realise how crucial storage choices are to AI success. Companies are spending big—AI infrastructure investments surged 37% in the first half of 2024, hitting $31.8 billion, per IDC. Yet, outdated storage solutions may be holding AI back.

An industry expert warns current AI ambitions might exceed actual capabilities due to messy data environments. Traditional storage was built for monolithic apps, not the distributed, cloud-native nature of AI workloads. Many organizations still rely on legacy systems, with inactive or inaccessible data stuck in inefficient storage structures.

Legacy Storage vs. Modern AI Demands

Traditional storage relies on rigid, siloed architectures that struggle with AI’s dynamic needs. Scaling them means expensive migrations or disruptive upgrades. AI workloads require flexible, high-speed storage that adapts quickly.

That’s where the likes of HPE Alletra Storage MP B10000 comes in. It’s a composable, scalable system designed for modern workloads. With NVMe optimization, up to 5.6PB capacity, and AMD EPYC™ processors, it delivers fast performance without traditional bottlenecks. The disaggregated design eliminates silos, allowing seamless expansion of capacity or performance as needed.