  Large Language Models (LLMs) have demonstrated remarkable capabilities in
important tasks such as natural language understanding and language generation,
and thus have the potential to make a substantial impact on our society. Such
capabilities, however, come with the considerable resources they demand,
highlighting the strong need to develop effective techniques for addressing
their efficiency challenges. In this survey, we provide a systematic and
comprehensive review of efficient LLMs research. We organize the literature in
a taxonomy consisting of three main categories, covering distinct yet
interconnected efficient LLMs topics from model-centric, data-centric, and
framework-centric perspective, respectively. We have also created a GitHub
repository where we organize the papers featured in this survey at
https://github.com/AIoT-MLSys-Lab/Efficient-LLMs-Survey. We will actively
maintain the repository and incorporate new research as it emerges. We hope our
survey can serve as a valuable resource to help researchers and practitioners
gain a systematic understanding of efficient LLMs research and inspire them to
contribute to this important and exciting field.
