Presented by F5As enterprises pour billions into GPU infrastructure for AI workloads, many are discovering that their expensive compute resources sit idle far more than expected. The culprit isn’t the hardware. It’s the often-invisible data delivery layer between storage and compute that’s starving GPUs of the information they need.”While people are focusing their attention, justifiably so, on GPUs, because they’re very significant investments, those are rarely the limiting factor,” says Mark Menger, solutions architect at F5. “They’re capable of more work. They’re waiting on data.”AI performance increasingly depends on an independent, programmable control point between AI frameworks and object storage — one that most enterprises haven’t deliberately architected. As AI workloads scale, bottlenecks and instability happens when AI frameworks are tightly coupled to specific storage endpoints during scaling events, failures, and cloud transitions.”Traditional storage access patterns were not designed for highly parallel, bursty, multi-consumer AI workloads,” says Maggie Stringfellow, VP, product management – BIG-IP. “Efficient AI data movement requires a distinct data delivery layer designed to abstract, optimize, and secure data flows independently of storage systems, because GPU economics make inefficiency immediately visible and expensive.”Why AI workloads overwhelm object storageThese bidirectional patterns include massive ingestion from continuous data capture, simulation output, and model checkpoints. Combined with read-intensive training and inference workloads, they stress the tightly coupled infrastructure upon which the storage systems are reliant.While storage vendors have done significant work in scaling the data throughput into and out of their systems, that focus on throughput alone creates knock-on effects across the switching, traffic management, and security layers coupled to storage.The stress on S3-compatible systems from AI workloa …
