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The burgeoning field of Large Language Models (LLMs), exemplified bysophisticated models like OpenAI's ChatGPT, represents a significantadvancement in artificial intelligence. These models, however, bring forthsubstantial challenges in the high consumption of computational, memory,energy, and financial resources, especially in environments with limitedresource capabilities. This survey aims to systematically address thesechallenges by reviewing a broad spectrum of techniques designed to enhance theresource efficiency of LLMs. We categorize methods based on their optimizationfocus: computational, memory, energy, financial, and network resources andtheir applicability across various stages of an LLM's lifecycle, includingarchitecture design, pretraining, finetuning, and system design. Additionally,the survey introduces a nuanced categorization of resource efficiencytechniques by their specific resource types, which uncovers the intricaterelationships and mappings between various resources and correspondingoptimization techniques. A standardized set of evaluation metrics and datasetsis also presented to facilitate consistent and fair comparisons acrossdifferent models and techniques. By offering a comprehensive overview of thecurrent sota and identifying open research avenues, this survey serves as afoundational reference for researchers and practitioners, aiding them indeveloping more sustainable and efficient LLMs in a rapidly evolving landscape.

Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models