Revisiting Disaggregated Large Language Model Serving for Performance and Energy Implications

Different from traditional Large Language Model (LLM) serving that colocates the prefill and decode stages on the same GPU, disaggregated serving dedicates distinct GPUs to prefill and decode workload. Once the prefill GPU completes its task, the KV cache must be transferred to the decode GPU. While existing works have proposed various KV cache transfer paths across different memory and storage tiers, there remains a lack of systematic benchmarking that compares their performance (e.g., latency and throughput) and energy efficiency. Meanwhile, although optimization techniques such as frequency scaling have been utilized for disaggregated serving, their performance and energy implications have not been rigorously characterized. In this paper, we address this gap through comprehensive benchmarking of disaggregated LLM serving. We evaluate multiple KV transfer paths under synthetic inference workloads, measuring latency, throughput, and energy consumption, and compare them against a colocated serving baseline. Through dynamic voltage and frequency scaling (DVFS), we further investigate the performance and energy consumption of disaggregated serving configurations under different GPU frequencies, and explore the benefits of independently frequency scaling on the prefill and decode GPUs. Our results show that performance gains from prefill-decode disaggregation are not guaranteed, but depend on multiple factors including the workload characteristic, KV transfer medium, and LLM model size. We also observe diminishing performance returns from increasing KV transmission bandwidth beyond a certain threshold. Moreover, we demonstrate that stage-wise independent frequency scaling provides additional opportunities to improve energy efficiency while satisfying latency SLO constraints. Based on these insights, we propose a prototype framework for SLO-aware energy-efficient LLM serving and discuss its design challenges and evaluation methodology.