AI-assisted Stochastic Optimization for GPU Data Centers Lifecycle Planning

The recent advancement in artificial intelligence (AI) boosts the demand for AI-related workloads, significantly increasing GPU deployment and associated power consumption in data centers. This trend accelerates hardware upgrades, introducing the need for GPU upgrades planning, reallocation, and retirement. Existing lifecycle planning methods typically rely on static provisioning or heuristic-based strategies, which make it challenging to address uncertainties in workload demand, hardware performance degradation, power limitations, etc. As a result, poor resource utilization and increased operating costs are common in data centers. We propose an AI-assisted stochastic optimization framework for GPU lifecycle planning in data centers. This framework utilizes large language models to generate predictive scenarios for future workload demands, hardware performance decay, etc., in different cases. These AI-generated insights are integrated into a stochastic optimization model, which helps stakeholders make decisions about the timing of upgrades and retirements of GPU and cooling systems in data centers.