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原文摘要

We perform a novel study of CPU micro-architectural characteristics on how they affect agentic AI CPU side latency. Small enterprises and individual users looking to reduce costs and seeking data privacy benefit from running agents locally, motivating an investigation into the optimal performance characteristics of an edge CPU to improve user experience. We investigate the relationship between CPU core frequency, CPU uncore frequency, active physical cores, and symmetric multi threading with agentic task latency in single and multi agent configurations. Across eight experiments on an Intel edge CPU, we find that P-core frequency produces the largest median latency improvement among the evaluated parameters. Increasing P-core frequency from 2.5 to 5.5 GHz yields a 1.154$\times$ speedup for a single agent and a 1.341$\times$ speedup for eight concurrent agents. We are the first to characterize hardware micro-architectural relationships on single and multi agent configurations with TerminalBench; we also provide an open source framework and dataset for repeatable CPU side latency agentic benchmarking.