  Large Language Models (LLMs) have attracted extensive attention due to their
remarkable performance across various tasks. However, the substantial
computational and memory requirements of LLM inference pose challenges for
deployment in resource-constrained scenarios. Efforts within the field have
been directed towards developing techniques aimed at enhancing the efficiency
of LLM inference. This paper presents a comprehensive survey of the existing
literature on efficient LLM inference. We start by analyzing the primary causes
of the inefficient LLM inference, i.e., the large model size, the
quadratic-complexity attention operation, and the auto-regressive decoding
approach. Then, we introduce a comprehensive taxonomy that organizes the
current literature into data-level, model-level, and system-level optimization.
Moreover, the paper includes comparative experiments on representative methods
within critical sub-fields to provide quantitative insights. Last but not
least, we provide some knowledge summary and discuss future research
directions.
