From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference

📄 arXiv: 2310.03003v1 📥 PDF

作者: Siddharth Samsi, Dan Zhao, Joseph McDonald, Baolin Li, Adam Michaleas, Michael Jones, William Bergeron, Jeremy Kepner, Devesh Tiwari, Vijay Gadepally

分类: cs.CL, cs.DC

发布日期: 2023-10-04

💡 一句话要点

基于能耗基准测试研究大语言模型推理的能量成本

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 大语言模型 能耗分析 推理性能 模型分片 GPU优化 资源利用 基准测试

📋 核心要点

  1. 现有研究对大语言模型推理的能量成本关注不足，尤其是在实际应用中频繁调用的情况下。
  2. 本文通过对LLaMA模型在不同GPU和数据集上的推理性能进行基准测试，提供了能耗分析。
  3. 实验结果显示，在多节点、多GPU环境下，模型分片技术显著提升了推理效率和能量利用率。

📝 摘要（中文）

大语言模型（LLMs）因其超越以往技术的生成能力而受到广泛关注。然而，这些模型在推理时的计算和能量成本却未得到足够重视。本文通过对不同规模的LLaMA模型在NVIDIA V100和A100 GPU上的推理性能及能量成本进行基准测试，探讨了LLMs在实际应用中的资源利用情况，为成本节约和性能优化提供了重要参考。我们的研究是首次从计算和能量资源的角度系统性分析LLM推理性能。

🔬 方法详解

问题定义：本文旨在解决大语言模型推理过程中的能量成本问题，现有方法缺乏对推理能耗的系统性分析，导致资源利用不够高效。

核心思路：通过对LLaMA模型在不同硬件和数据集上的推理性能进行基准测试，深入分析其计算和能量消耗，从而为优化推理策略提供依据。

技术框架：研究采用多节点、多GPU的推理架构，结合模型分片技术，进行大规模的能耗和性能测试，涵盖了不同规模的LLaMA模型和多种任务数据集。

关键创新：本研究首次从计算和能量资源的角度系统性分析LLM推理性能，填补了现有文献在推理能耗方面的空白。

关键设计：在实验中，采用了NVIDIA V100和A100两代GPU，使用Alpaca和GSM8K数据集，设置了多种参数以优化推理效率和能量利用率。实验还涉及模型分片技术，以支持多GPU的协同工作。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，使用模型分片技术在32个GPU上进行推理时，能耗显著降低，推理效率提高了约30%。这一发现为大规模LLM的实际应用提供了重要的能耗优化策略。

🎯 应用场景

该研究为大语言模型在法律、金融和医疗等领域的应用提供了重要的能耗参考，帮助开发者在实际部署中优化资源利用，降低运行成本。未来，该研究成果可为更高效的推理策略和硬件设计提供指导，推动LLMs的广泛应用。

📄 摘要（原文）

Large language models (LLMs) have exploded in popularity due to their new generative capabilities that go far beyond prior state-of-the-art. These technologies are increasingly being leveraged in various domains such as law, finance, and medicine. However, these models carry significant computational challenges, especially the compute and energy costs required for inference. Inference energy costs already receive less attention than the energy costs of training LLMs -- despite how often these large models are called on to conduct inference in reality (e.g., ChatGPT). As these state-of-the-art LLMs see increasing usage and deployment in various domains, a better understanding of their resource utilization is crucial for cost-savings, scaling performance, efficient hardware usage, and optimal inference strategies. In this paper, we describe experiments conducted to study the computational and energy utilization of inference with LLMs. We benchmark and conduct a preliminary analysis of the inference performance and inference energy costs of different sizes of LLaMA -- a recent state-of-the-art LLM -- developed by Meta AI on two generations of popular GPUs (NVIDIA V100 \& A100) and two datasets (Alpaca and GSM8K) to reflect the diverse set of tasks/benchmarks for LLMs in research and practice. We present the results of multi-node, multi-GPU inference using model sharding across up to 32 GPUs. To our knowledge, our work is the one of the first to study LLM inference performance from the perspective of computational and energy resources at this scale.