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AI Greenferencing: Routing AI Inferencing to Green Modular Data Centers with Heron

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arxiv 2505.09989 v1 pith:KY3JZCOL submitted 2025-05-15 cs.DC cs.AIcs.NI

classification cs.DCcs.AIcs.NI
keywords powerwindcomputeheroninferencingworkloadfarmsgreen
verification ladder T0 review T1 audit T2 compute T3 formal
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AI power demand is growing unprecedentedly thanks to the high power density of AI compute and the emerging inferencing workload. On the supply side, abundant wind power is waiting for grid access in interconnection queues. In this light, this paper argues bringing AI workload to modular compute clusters co-located in wind farms. Our deployment right-sizing strategy makes it economically viable to deploy more than 6 million high-end GPUs today that could consume cheap, green power at its source. We built Heron, a cross-site software router, that could efficiently leverage the complementarity of power generation across wind farms by routing AI inferencing workload around power drops. Using 1-week ofcoding and conversation production traces from Azure and (real) variable wind power traces, we show how Heron improves aggregate goodput of AI compute by up to 80% compared to the state-of-the-art.

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Cited by 1 Pith paper

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  1. Enabling Spatially Fine-Grained DVFS in Neural Processing Units for Energy-Efficient LLM Serving

    cs.AR 2026-07 conditional novelty 7.0 of 10

    Component-level DVFS on NPUs, with pipeline refactoring and compiler-coordinated voltage/frequency selection, cuts LLM-serving energy by 25.8–35.2% at sub-4% area overhead in simulation.

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