mlsanityData to 14 Oct 2026

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

arXiv 2505.09989 ↗First posted 15 May 2025cs.DC cs.AI cs.NIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 67 references that are in the map. First posted on arXiv 15 May 2025.

Builds on

9 of 67 references.

References · 9

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Modelscited by 3
  2. 2025VoltanaLLM: Energy-Efficient and SLO-Aware Disaggregated LLM Serving via Adaptive Frequency Control and State-Space Routingcited by 2
  3. 2026Enabling Spatially Fine-Grained DVFS in Neural Processing Units for Energy-Efficient LLM Servingcited by 0