DualScale: Energy-Efficient Disaggregated LLM Serving via Phase-Aware Placement and DVFS

TL;DR

DualScale is a hierarchical energy optimization framework for disaggregated LLM serving that dynamically manages placement and GPU frequency to reduce energy consumption while meeting strict latency and throughput SLOs.

Contribution

It introduces a two-tier control system combining phase-aware placement and stage-specific frequency adaptation for energy-efficient LLM serving.

Findings

Reduces energy by up to 39% in prefill and 48% in decode.

Successfully meets TTFT and TPOT SLOs in a 16x H100 cluster.

Employs predictive models and stage-specific control for dynamic adaptation.

Abstract

Prefill/decode disaggregation is increasingly adopted in LLM serving to improve the latency-throughput tradeoff and meet strict TTFT and TPOT SLOs. However, LLM inference remains energy-hungry: autoscaling alone is too coarse-grained to track fast workload fluctuations, and applying fine-grained DVFS under disaggregation is complicated by phase-asymmetric dynamics and coupling between provisioning and frequency control. We present DualScale, a two-tier energy optimization framework for disaggregated LLM serving. DualScale jointly optimizes placement and DVFS across prefill and decode using predictive latency and power models. At coarse timescales, DualScale computes phase-aware placement and baseline frequencies that minimize energy while satisfying SLO constraints. At fine timescales, DualScale dynamically adapts GPU frequency per iteration using stage-specific control: model predictive control (MPC) for prefill to account for queue evolution and future TTFT impact, and lightweight slack-aware adaptation for decode to exploit its smoother, memory-bound dynamics. This hierarchical design enables coordinated control across timescales while preserving strict serving SLOs. Evaluation on a 16x H100 cluster serving Llama 3.3 70B with production-style traces shows that DualScale meets TTFT/TPOT SLOs while reducing energy by up to 39% in prefill and 48% in decode relative to DistServe.