A Review on Proprietary Accelerators for Large Language Models

Sihyeong Park, Jemin Lee, Byung-Soo Kim, Seokhun Jeon

Abstract: With the advancement of Large Language Models (LLMs), the importance of accelerators that efficiently process LLM computations has been increasing. This paper discusses the necessity of LLM accelerators and provides a comprehensive analysis of the hardware and software characteristics of the main commercial LLM accelerators. Based on this analysis, we propose considerations for the development of next-generation LLM accelerators and suggest future research directions.