将大模型推理部署到风电场，用实时负载动态调度，降低延迟近98%。
CWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms
- 基于实时延迟、缓存利用率和队列深度，动态调度推理请求
- 在真实64-GPU测试中，P99延迟比基线低98%
- 适合关注绿色算力与边缘推理优化的研究者与工程师
AI算力需求以空前速度增长，而电网常因扩容成本高、远距离输电损耗大而难以应对。风电场虽有丰富可再生能源，却缺乏匹配的算力需求。本文提出「AI Greeninferencing」模式，将模块化AI计算部署至风电场，实现本地化算力扩张，生成场站内部用电需求，缓解电网压力。可行性分析显示，890+吉瓦风能装机容量位于Azure数据中心50毫秒网络往返时延范围内；通过站点按需配置与风电空间互补性，整体集群利用率与传统部署相当。为应对波动的风电供电，我们构建轻量级、响应式、无工作负载依赖的推理路由器CWind，仅使用实时信号（延迟、KV缓存利用率、队列深度）动态调整站点与请求分发。在模拟三个风电场的64张A100 GPU测试平台，采用Azure生产级流量数据评估，CWind相比最强对比方案（同属本研究）将P99端到端延迟降低52%，相比功率限制和GPU空闲等基线方案降低高达98%，且在不同工作负载、负载水平和GPU代际下均保持稳定收益。
原文摘要 · Abstract (English)
AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up. Grid expansion comes with high capital expenditure and long-distance transmission losses, yet there is abundant renewable energy at the source, just not matched to demand. This paper proposes a complementary AI infrastructure deployment model, AI Greeninferencing, that brings modular AI compute to renewable energy sources, focusing on wind, allowing AI footprint expansion, generating local behind-the-meter demand for renewable sites, and helping ease the growing strain on power utilities. Our feasibility analysis shows that 890+ GW of wind capacity lies within 50 ms network round trip time of Azure data centers, and that site-wise right-sizing combined with spatial complementarity of wind energy keeps aggregate fleet utilization on par with traditional deployments. To serve inference requests under variable wind power, we build CWind, a lightweight, reactive, and workload-agnostic AI inference router that uses only real-time signals: inference latency, KV-cache utilization, and queue depth, to dynamically configure sites and distribute requests. Evaluated on a real 64-GPU A100 testbed emulating three wind-powered sites with Azure production traces, CWind reduces P99 end-to-end latency by up to 52% over the strongest contender (also our idea) and by up to 98% over baselines such as power-capping and GPU idling, with consistent gains across workload types, load levels, and GPU generations.
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