A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms

However, their remarkable capabilities come with significant computational and memory demands. This has raised considerable challenges when deploying these models in scenarios with limited resources or high concurrency. To address these challenges, low-bit quantization has emerged as a pivotal approach for enhancing the efficiency and deployability of LLMs. Low-bit quantization involves the process of reducing the bit-width of tensors, which effectively decreases the memory footprint and computational requirements of LLMs. By compressing weights, activations, and gradients of LLMs with low-bit integer/binary representation, quantization can significantly accelerate inference and training and reduce storage requirements with acceptable accuracy. This efficiency is crucial for enabling advanced LLMs to be accessible on devices with constrained resources, thereby broadening their applicability. In this paper, we aim to provide a survey with a comprehensive overview of low-bit quantization for large language models (LLMs), encompassing the fundamental concepts, system implementations, and algorithmic approaches related to low-bit LLMs. Compared with the traditional models, LLMs, as the representative paradigm of the foundation model, always feature a vast number of parameters, which presents unique challenges for effective quantization. As depicted in Figure 1, Section 2 introduces the fundamentals of low-bit quantization of LLMs, including new low-bit data formats and quantization granularities specific to LLMs.