Artificial intelligence (AI) is driving rapid growth in electricity demand, yet the grid-facing power dynamics of AI data centers remain poorly understood. Here we show that, in shared-GPU systems, the composition of batch and inference workloads decouples aggregate power variability from short-horizon ramping. As the inference share rises, variability becomes U-shaped, whereas ramping becomes hump-shaped, particularly under higher loading. The magnitude and turning points of these patterns also depend on system loading. Using a trace-calibrated framework linking workload arrivals, queueing, scheduling, and GPU power, we show that the underlying mechanism is asymmetric. At intermediate workload mixes, queued batch jobs fill capacity left idle by fluctuating inference demand, reducing aggregate power variability. However, short-horizon ramping remains elevated because inference-side fluctuations propagate more directly into realized power. AI data centers should therefore be understood as dynamic systems whose workload composition shapes their grid impact.

翻译：人工智能（AI）正在推动电力需求的快速增长，然而AI数据中心与电网交互的功率动态特性仍未被充分理解。本文证明，在共享GPU系统中，批量任务与推理任务的负载组合能够将聚合功率波动与短周期爬坡行为解耦。随着推理任务占比上升，功率波动呈现U形变化，而爬坡幅度呈现驼峰形变化，尤其是在高负载条件下。这些模式的幅度和转折点还取决于系统负载水平。通过构建融合任务到达、排队、调度与GPU功耗的轨迹校准框架，我们发现其底层机制具有非对称性：在中等负载组合状态下，排队的批量任务可填充推理任务波动所遗留的空闲容量，从而降低聚合功率波动；但短周期爬坡幅度仍然较高，这是因为推理侧波动会以更直接的方式传递至实际功率。因此，AI数据中心应被理解为动态系统，其负载组合结构将深刻影响对电网的影响。