$e$PACT: Energy-Performance-Aware Commitment Tracking for LLM Serving

The power grid must continuously balance electricity supply and demand, making predictable demand important for electricity provisioning. Data center operators therefore often procure electricity under contracts that specify hourly energy commitments, based on which the expected demand can be planned in advance. Deviating from these commitments in either direction can lead to unfavorable settlements or penalties. For data centers serving increasingly dynamic LLM workloads, this creates a new systems challenge: serving decisions directly affect energy consumption, yet the realized consumption must track a precommitted hourly target while preserving request-level SLOs. We formulate this problem as energy-Performance-Aware Commitment Tracking and implement $e$PACT, an LLM serving system built around a two-level controller that periodically revisits the energy vs SLO-performance trade-off and adjusts the serving configuration. A global planner updates near-term energy targets from the remaining hourly commitment and observed consumption, while a local decision maker simulates candidate active-replica and GPU-clock configurations and selects the SLO-feasible action that best matches the target under asymmetric deviation costs. Adaptive action search limits planning overhead while serving proceeds asynchronously. We evaluate $e$PACT against representative latency-first and energy-first baselines, showing its ability to maintain service quality while better aligning realized energy consumption with varying hourly commitments.