LLMEnergyMeasure: An Industrial-Grade Benchmark Framework for Energy Efficiency Evaluation of Large Language Model Inference

LLMEnergyMeasure is a research framework for the inference efficiency of large language models (LLMs), providing MLPerf-style benchmark tests to comprehensively evaluate LLM inference performance from three dimensions: energy consumption, throughput, and computational complexity. It aims to fill the gap where existing tools ignore energy consumption, assist enterprises in scenarios such as hardware selection, optimization strategy verification, and carbon footprint accounting, and promote the sustainable development of the AI industry.

The inference cost of large language models rises sharply with model size, and energy efficiency ratio has become a key indicator for enterprises to deploy AI services. Existing benchmark tools mostly focus on throughput and latency, ignoring the energy consumption dimension. There is a lack of a unified energy efficiency comparison standard between different hardware platforms and optimization strategies, making it difficult for decision-makers to make optimal choices. The LLMEnergyMeasure project was born to fill this gap.

LLMEnergyMeasure builds a comprehensive evaluation framework to measure LLM inference efficiency from three core dimensions:

1. Energy efficiency: Measured in joules per token (J/token), supporting three measurement methods: software telemetry (NVIDIA Management Library/NVML, Intel RAPL interface), hardware power meters, and energy integration;
2. Inference throughput: Distinguishing between time-to-first-token (TTFT) and sustained throughput (tok/s), reflecting user experience and system capacity;
3. Computational complexity: Counting floating-point operations (FLOPs) to assist hardware selection and cost estimation.

LLMEnergyMeasure draws on MLPerf industry standard practices to ensure the comparability and reproducibility of test results:

• Standardized test loads: Covering typical application scenarios such as short text generation, long text continuation, batch inference, and mixed loads;
• Strict preheating and stabilization: Sufficient preheating before formal testing to avoid cold start effects, and ensuring data reliability through multiple sampling;
• Reproducible experimental configuration: Complete recording of test parameters, environment configuration, and random seeds to ensure consistent experimental results at different times and locations.

The application scenarios of LLMEnergyMeasure include:

1. Hardware selection decision: Comparing energy efficiency indicators of different GPUs, CPUs, or AI accelerators to select devices suitable for business scenarios;
2. Optimization strategy verification: Quantifying changes in energy consumption, throughput, and accuracy of model optimization technologies such as pruning and distillation;
3. Carbon footprint accounting: Providing accurate energy consumption data as the basic input for ESG carbon footprint calculation;
4. Service pricing reference: Formulating reasonable pricing strategies based on the energy cost of a single inference.

The framework adopts a modular design, with core components including a measurement engine (collecting performance and power consumption data), a load generator (generating standardized test requests), a result analyzer (processing raw data to generate reports), and a visualization module (drawing performance curves and comparison charts); it supports multiple inference backends (Hugging Face Transformers, vLLM, TensorRT-LLM, llama.cpp); reserved extension interfaces allow integration of custom indicators such as memory usage and video memory bandwidth utilization through plugins.

The emergence of LLMEnergyMeasure coincides with the context of global carbon neutrality and rising energy costs, and the energy efficiency issue in the AI industry is receiving increasing attention. This open-source framework provides a fair and transparent energy efficiency evaluation benchmark for academia and industry. We look forward to:

• Hardware manufacturers using this framework for product energy efficiency certification;
• Cloud service providers disclosing energy efficiency indicators of LLM services;
• Researchers publishing green AI-related papers based on this framework;
• The open-source community contributing more optimization strategies and measurement methods.

LLMEnergyMeasure is not only a technical tool but also an important infrastructure to promote the sustainable development of the AI industry. By establishing a unified energy efficiency evaluation standard, it helps developers find the optimal balance between performance, cost, and environmental protection. With the popularization of large language model applications, this tool will become a must-have for LLM deployment teams.