Optimizing LLM Inference for Low Power Consumption

LLM inference at scale is an energy-intensive workload. As model sizes grow and context windows expand, the power drawn by GPU clusters has become a significant operational cost and a sustainability concern. For teams running production workloads, optimizing for low power consumption is no longer just an environmental preference. It is a direct lever for cost control and hardware longevity.

Why Power Matters in Production Inference

Power in transformer inference is consumed during two distinct phases: prefill and decode. Prefill processes the input prompt in parallel, while decode generates output tokens autoregressively. Both scale with model dimension, layer count, and active parameter count, but prefill is compute-bound and decode is memory-bandwidth-bound. Reducing energy therefore requires attacking both phases: choosing architectures with fewer active parameters, minimizing prompt length, and maximizing hardware utilization so that every watt translates to useful throughput rather