BEAM: Joint Resource–Power Optimization for Energy-Efficient LLM Inference under SLO contraints

Large Language Model (LLM) serving is rapidly becoming one of the most power-intensive workloads in modern datacenters. Unlike training, where throughput dominates, inference must satisfy strict per-request latency targets such as Time-to-First-Token (TTFT) and Time-Between-Tokens (TBT). Once an SLO is met, the remaining latency slack between the earliest possible completion and the deadline offers an opportunity for energy savings. Existing systems, however, exploit only one dimension of this trade-off: batching improves resource efficiency, while DVFS improves power efficiency. These two axes are tightly coupled, and optimizing one while fixing the other yields only a local optimum. We present BEAM, a fine-grained controller that dynamically co-optimizes resource and power efficiency under per-request SLOs. BEAM continuously allocates the available latency slack across both dimensions by jointly tuning GPU frequency, chunk size, and microbatch count in real time. Its event-driven design responds instantly to request arrivals and completions, while a lightweight predictive model enables sub-millisecond decision making with negligible overhead. Implemented atop the vLLM runtime, BEAM reduces end-to-end GPU energy consumption by up to 51\% compared to vLLM.