GPU-Accelerated Storage Fundamentals #1 — Why AI Needs a New Kind of Storage-- Extremely High Input-Output Per Second Storage
Modern AI workloads shift storage demands from bandwidth to high IOPS and concurrency. GPUs require continuous small, random data access (512B–8KB), making data delivery—not compute—the main bottleneck. Traditional CPU-centric storage cannot sustain the required parallelism, leading to underutilized GPUs. As a result, storage must evolve from capacity-focused (TB/TCO) to concurrency-driven (IOPS/TCO), becoming an active, high-performance data layer for AI systems.
