Over the past few years, AI infrastructure has been designed primarily around model training, with ever-larger clusters, faster accelerators, and higher bandwidth\u2014all optimized to keep GPUs running at full utilization. That design approach is beginning to shift. As AI workloads shift toward inference, data centers are encountering a new bottleneck\u2014not just computational speed, but the ability to efficiently store, manage, and serve the expanding volumes of memory-resident data that inference requires. This transition is significant because training and inference place very different demands on infrastructure. Training is primarily a compute-and-bandwidth challenge. The objective is to maximize throughput by executing tightly synchronized bursts of computation, rapidly transferring massive quantities of model parameters, activations, and gradients across accelerators. In that setting, memory is tuned for high speed, strong locality, and abundant bandwidth. The system is engineered to ensure that costly compute resources remain fully utilized. Related: Scaling the Memory Wall: HBM, CXL, and the New GPU Playbook. Inference alters the equation. After a model is deployed, the main challenge extends beyond simply running mathematical operations at maximum speed.<\/p>\n
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