The situation also has fundamental implications for data storage. Traditional storage was built for predictable, sequential workloads like databases and virtualization. AI upends that model, with thousands of GPU threads hammering existing systems with parallel, random, high-throughput access.
The performance problems this can create cascade across infrastructure components. When storage cannot keep up, GPUs sit idle, training cycles stall and overall costs soar. Every hour of underfed GPUs delays ROI because training is an investment and stalled or inefficient epochs push out time to value. The risks extend even further. If data is corrupted or lost, entire models often need to be retrained, creating enormous and unexpected costs. The impact goes beyond training inefficiency. Inference is the revenue-generating component, and slow or unstable data pipelines directly reduce the commercial return of AI applications. In response, legacy vendors are trying to retrofit existing architectures to meet AI demand, but despite their best efforts, most of these designs still limit performance and scalability.
Something has to give, starting with the recognition that AI requires purpose-built, natively high-performance storage systems.
