A Survey on Inference Engines for Large Language Models: Perspectives on
  Optimization and Efficiency
Authors: name: Sihyeong Park, name: Sungryeol Jeon, name: Chaelyn Lee, name: Seokhun Jeon, name: Byung-Soo Kim, name: Jemin Lee

Large language models (LLMs) are widely applied in chatbots, code generators,
and search engines. Workloads such as chain-of-thought, complex reasoning, and
agent services significantly increase the inference cost by invoking the model
repeatedly. Optimization methods such as parallelism, compression, and caching
have been adopted to reduce costs, but the diverse service requirements make it
hard to select the right method. Recently, specialized LLM inference engines
have emerged as a key component for integrating the optimization methods into
service-oriented infrastructures. However, a systematic study on inference
engines is still lacking. This paper provides a comprehensive evaluation of 25
open-source and commercial inference engines. We examine each inference engine
in terms of ease-of-use, ease-of-deployment, general-purpose support,
scalability, and suitability for throughput- and latency-aware computation.
Furthermore, we explore the design goals of each inference engine by
investigating the optimization techniques it supports. In addition, we assess
the ecosystem maturity of open source inference engines and handle the
performance and cost policy of commercial solutions. We outline future research
directions that include support for complex LLM-based services, support of
various hardware, and enhanced security, offering practical guidance to
researchers and developers in selecting and designing optimized LLM inference
engines. We also provide a public repository to continually track developments
in this fast-evolving field:
https://github.com/sihyeong/Awesome-LLM-Inference-Engine