想省电又怕买错GPU？WattGPU用公开数据就能预测新GPU和新LLM的功耗和速度，不用自己跑实验，误差才几个点。
WattGPU提出了两个预测模型，分别用于平均GPU功耗和令牌间延迟（ITL），仅利用公开的LLM元数据和GPU规格，无需硬件访问或预配置。在42个开源LLM（0.1B-27B参数）和8个GPU的数据集上，通过留一GPU和留一LLM交叉验证，功耗模型在离线场景下中位数绝对百分比误差≤3.4%，服务器场景下≤13.5%；延迟模型在服务器模式下≤8.5%，且GPU排名相关性Kendall τ≥0.76。与基于功耗的热设计功率（TDP）和基于延迟的roofline基线相比，WattGPU在未见过LLM-GPU组合上误差降低约4倍，在完全未见过GPU上降低约2倍。
WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs
Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination. While some predictive models exist, they still require profiling data and struggle to generalize to hardware unseen during training. To address this, we introduce \textit{WattGPU}, featuring two predictive models for mean GPU power draw and Inter-Token Latency (ITL). Our approach leverages only publicly available LLM metadata and GPU specifications, eliminating the need for hardware access or profiling while enabling generalization to unseen NVIDIA server-grade GPUs and LLMs. We evaluate our models using rigorous leave-one-GPU-out and leave-one-LLM-out cross-validation on a dataset of 42 open-source LLMs (0.1B--27B parameters) and 8 GPUs under both offline and server scenarios. The mean power draw model achieves a median absolute percentage error of $\leq3.4\%$ for offline and $\leq13.5\%$ for server scenarios on unseen GPUs, while the latency model achieves $\leq8.5\%$ in server mode, both maintaining strong GPU ranking correlations for server scenarios (Kendall $τ\geq0.76$). Compared to standard physically grounded baselines -- Load-Scaled Thermal Design Power (TDP) for power draw and roofline for latency -- our models reduce median absolute percentage error by approximately 4$\times$ on unseen LLM-GPU combinations for server scenarios or approximately 2$\times$ for completely unseen GPUs. WattGPU's data and code are publicly available at https://github.com/maufadel/wattgpu.