Resource-Efficient Language Models: Quantization for Fast and Accessible Inference
It addresses hardware and energy challenges for end-users by providing an overview of existing methods, making it incremental.
This paper reviews post-training quantization techniques to reduce the resource demands of large language models for more accessible and energy-efficient inference, summarizing various schemes and trade-offs.
Large language models have significantly advanced natural language processing, yet their heavy resource demands pose severe challenges regarding hardware accessibility and energy consumption. This paper presents a focused and high-level review of post-training quantization (PTQ) techniques designed to optimize the inference efficiency of LLMs by the end-user, including details on various quantization schemes, granularities, and trade-offs. The aim is to provide a balanced overview between the theory and applications of post-training quantization.