
The traditional reliance on central processing units to manage data flows has become the primary bottleneck preventing modern artificial intelligence from reaching its theoretical peak performance. As computational demands for large-scale model training and real-time inference skyrocket, the industry is forced to look beyond raw floating-point operations. The current movement toward AI-optimized storage infrastructure represents a fundamental shift in how










