Of the infrastructure trifecta – compute, networking, and storage – storage has long been the odd one out. While GPUs grabbed headlines and networking components raced to keep pace with workloads moving at the speed of light, storage sat in the background, a solved problem as it were – or so the thinking went.
That assumption is now under serious pressure. AI is consuming stored data, but it’s also generating it at unprecedented scales, a pace that existing architectures weren't designed to handle. Storage has moved from afterthought to critical path, and the engineering community is scrambling to catch up.
SDxCentral's latest supplement examines what that scramble looks like in practice, with in-depth coverage of:
How AI is altering data storage scale, structure, and sanity: AI is generating tons of data, but can storage keep up?
Why KV cache is key to AI memory woes: Exploring key-value cache considerations with Weka, Vast, and Red Hat
TurboQuant: The real story behind how a memory compression algorithm went viral
Storage hardware in space: The romantic idea of outer space exploration meets the unglamorous world of incremental hardware gains
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