SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving

• URL: http://arxiv.org/abs/2408.05235v1
• Date: Mon, 5 Aug 2024 09:07:06 GMT
• Title: SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving
• Authors: Andreas Kosmas Kakolyris, Dimosthenis Masouros, Petros Vavaroutsos, Sotirios Xydis, Dimitrios Soudris,
• Abstract summary: We present textitthrottLL'eM, a framework that reduces energy consumption while meeting Service-Level Objectives. textitthrottLL'eM features mechanisms that project future KV cache usage and batch size. We show that the proposed ML model achieves $R2$ scores greater than 0.97 and miss-predicts performance by less than 1 iteration per second on average.
• Score: 6.010159688581912
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As Large Language Models (LLMs) gain traction, their reliance on power-hungry GPUs places ever-increasing energy demands, raising environmental and monetary concerns. Inference dominates LLM workloads, presenting a critical challenge for providers: minimizing energy costs under Service-Level Objectives (SLOs) that ensure optimal user experience. In this paper, we present \textit{throttLL'eM}, a framework that reduces energy consumption while meeting SLOs through the use of instance and GPU frequency scaling. \textit{throttLL'eM} features mechanisms that project future KV cache usage and batch size. Leveraging a Machine-Learning (ML) model that receives these projections as inputs, \textit{throttLL'eM} manages performance at the iteration level to satisfy SLOs with reduced frequencies and instance sizes. We show that the proposed ML model achieves $R^2$ scores greater than 0.97 and miss-predicts performance by less than 1 iteration per second on average. Experimental results on LLM inference traces show that \textit{throttLL'eM} achieves up to 43.8\% lower energy consumption and an energy efficiency improvement of at least $1.71\times$ under SLOs, when compared to NVIDIA's Triton server.

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