From autonomous agents to biomedical breakthroughs, today's most demanding AI workloads depend on mining intelligence from vast pools of unstructured data. High-performance storage is necessary, but it isn't sufficient on its own. Training runs spanning thousands of GPUs, retrieval-augmented pipelines, and agents that loop through tools and shared state all need a single, high-throughput data plane that every GPU, container, and analytics engine can hit at once.
Some architectures were designed for that. Others were designed for render farms and home directories, and are showing the strain. See how four AI storage architectures actually compare, and why shared-nothing scale-out hits a ceiling that doesn't have to be yours.
