As generative AI moves from experimentation into production, inference is becoming the defining challenge for enterprise AI. Adding more GPUs alone cannot solve rising latency, concurrency, and token-cost pressures; the way data and KV cache move across the infrastructure stack is increasingly critical. In this session, Nikhil Madan will examine how a tiered approach spanning GPU memory, CPU memory, local NVMe, and shared high-performance storage can remove inference bottlenecks. Attendees will learn how better KV-cache orchestration can reduce time to first token, increase throughput and concurrency, improve GPU utilization, and lower the cost of serving AI at without continually expanding costly GPU infrastructure.