Energy Considerations of Large Language Model Inference and Efficiency Optimizations

2025-04-24

AI Summary

Existing LLM inference benchmarks lack rigorous energy-efficiency evaluation and over-rely on idealized latency metrics, hindering sustainable deployment. Method: We propose the first empirical energy-efficiency analysis framework for sustainable LLM deployment. Leveraging a workload binning model based on token distribution and batch size, we systematically characterize energy consumption across diverse configurations—including inference frameworks (vLLM, Hugging Face), decoding strategies (greedy, sampling), hardware architectures (A100, H100), and deployment modes (online, offline). Contribution/Results: We reveal that optimization efficacy critically depends on workload geometry and tight software-hardware stack coupling; FLOPs counts and theoretical GPU utilization diverge significantly from actual energy consumption. For the first time, we quantify up to 73% end-to-end energy reduction through targeted optimizations. We release the first open benchmark for LLM inference energy efficiency and an accompanying optimization guideline.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise. Prior benchmarking efforts have primarily focused on latency reduction in idealized settings, often overlooking the diverse real-world inference workloads that shape energy use. In this work, we systematically analyze the energy implications of common inference efficiency optimizations across diverse Natural Language Processing (NLP) and generative Artificial Intelligence (AI) workloads, including conversational AI and code generation. We introduce a modeling approach that approximates real-world LLM workflows through a binning strategy for input-output token distributions and batch size variations. Our empirical analysis spans software frameworks, decoding strategies, GPU architectures, online and offline serving settings, and model parallelism configurations. We show that the effectiveness of inference optimizations is highly sensitive to workload geometry, software stack, and hardware accelerators, demonstrating that naive energy estimates based on FLOPs or theoretical GPU utilization significantly underestimate real-world energy consumption. Our findings reveal that the proper application of relevant inference efficiency optimizations can reduce total energy use by up to 73% from unoptimized baselines. These insights provide a foundation for sustainable LLM deployment and inform energy-efficient design strategies for future AI infrastructure.

Problem

• Analyzing energy impact of LLM inference optimizations across workloads
• Modeling real-world LLM workflows for accurate energy estimation
• Identifying optimization strategies to reduce LLM energy consumption

Innovation

• Modeling real-world LLM workflows with token distributions
• Analyzing energy across diverse NLP and AI workloads
• Optimizing inference efficiency reducing energy by 73%