A Comprehensive Study on Quantization Techniques for Large Language Models
This addresses the problem of high computational demands for LLMs in IoT and embedded systems, but it is incremental as it synthesizes existing knowledge rather than introducing novel methods.
The paper tackles the computational and storage challenges of deploying large language models (LLMs) on resource-constrained devices by providing a comprehensive analysis of quantization techniques, reviewing methods and their performance outcomes without presenting new experimental results.
Large Language Models (LLMs) have been extensively researched and used in both academia and industry since the rise in popularity of the Transformer model, which demonstrates excellent performance in AI. However, the computational demands of LLMs are immense, and the energy resources required to run them are often limited. For instance, popular models like GPT-3, with 175 billion parameters and a storage requirement of 350 GB, present significant challenges for deployment on resource-constrained IoT devices and embedded systems. These systems often lack the computational capacity to handle such large models. Quantization, a technique that reduces the precision of model values to a smaller set of discrete values, offers a promising solution by reducing the size of LLMs and accelerating inference. In this research, we provide a comprehensive analysis of quantization techniques within the machine learning field, with a particular focus on their application to LLMs. We begin by exploring the mathematical theory of quantization, followed by a review of common quantization methods and how they are implemented. Furthermore, we examine several prominent quantization methods applied to LLMs, detailing their algorithms and performance outcomes.