AI infrastructure companies raised $100 million in Series A funding to address the memory wall—a critical bottleneck where GPU compute speeds vastly outpace memory bandwidth, forcing enterprises to overprovision expensive hardware just to access sufficient memory capacity.

This imbalance represents the most pressing constraint in scaling AI workloads today. As Stanford’s 2025 AI Index Report shows, training clusters double every five months while essential memory infrastructure lags years behind, creating costly inefficiencies that ripple throughout enterprise AI deployments.