InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centers
The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality. Yet operators often need to compare deployment alternatives before large-scale infrastructure is built, making direct measurement costly, slow, and sometimes infeasible. We present InFactPlanner, a trace-driven decision-support framework for what-if analysis of sustainable AI data center deployment for LLM inference across single and geo-distributed sites. InFactPlanner combines query traces, hardware-model profiles, candidate site configurations, PUE/WUE parameters, re...
| Authors | Nicoletta Tsiopani, Moysis Symeonides, George Pallis et al. |
|---|---|
| Primary category | cs.DC |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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