Aging-aware CPU Core Management for Embodied Carbon Amortization in Cloud LLM Inference

Abstract

Broad adoption of Large Language Models (LLM) demands rapid expansions of cloud LLM inference clusters, leading to accumulation of embodied carbon$-$the emissions from manufacturing and supplying IT assets$-$that mostly concentrate on inference server CPU. This paper delves into the challenges of sustainable growth of cloud LLM inference, emphasizing extended amortization of CPU embodied over an increased lifespan. Given the reliability risks of silicon aging, we propose an aging-aware CPU core management technique to delay CPU aging effects, allowing the cluster operator to safely increase CPU life. Our technique exploits CPU underutilization patterns that we uncover in cloud LLM inference by halting aging in unused cores and even-outing aging in active cores via selective deep idling and aging-aware inference task allocation. Through extensive simulations using real-world Azure inference traces and an extended LLM cluster simulator from Microsoft, we show superior performance of our technique over existing methods with an estimated 37.67\% reduction in yearly embodied carbon emissions through p99 performance of managing CPU aging effects, a 77\% reduction in CPU underutilization, and less than 10\% impact to the inference service quality.