Routing LLM Inference to the Cleanest Grid in Real Time

Abstract

Large-language-model inference is a fast-growing electricity load whose marginal carbon intensity varies by more than an order of magnitude across grid regions and across the day, making request placement an attractive lever: no retraining, no hardware change. We report a live validation of carbon-aware inference routing on multi-region GPU testbeds driven by marginal operating emissions rate (MOER) signals, with three properties uncommon in prior work: a blind baseline that is an actual production pressure-based router rather than uniform placement; per-request energy attributed from GPU telemetry (NVIDIA DCGM) via measured concurrency curves rather than nameplate TDP; and carbon settlement of every request against historical MOER, not only the forecast that drove the decision. The central live result is feasibility: a MOER signal steered inference across regions with no observed dispatch failures, as a strict and reversible overlay on the production router. To size the effect, we replay a year of hourly MOER across a grid-diverse CONUS fleet. In the primary modeled configuration, carbon-aware placement reduces modeled GPU-attributable operational emissions by 50.9% versus round-robin (95% block-bootstrap CI 48.5-53.3%). Because the replay dispatches against historical MOER rather than a forecast, this is an upper bound under that configuration; forecast error would reduce operationally realized savings. Before session pinning, hourly lowest-MOER routing contributes about 22.4 percentage points, roughly 40% of the 54.0% placement reduction, beyond a static annual-mean-MOER policy. These are modeled results for one fleet and historical year, not a universal estimate. We also record a practical observation: when comparing regions, rank by absolute MOER rather than the percentile signal-index, which is normalized within each region and answers a temporal, not a spatial, question.