Low Bit Quantization

Low-bit quantization aims to reduce the memory footprint and computational cost of large language models (LLMs) and other deep learning models by representing their weights and activations using fewer bits, thereby accelerating inference and enabling deployment on resource-constrained devices. Current research focuses on developing novel quantization algorithms, including post-training quantization (PTQ) and quantization-aware training (QAT) methods, often tailored to specific model architectures like transformers and convolutional neural networks. These advancements are significant because they address the critical bottleneck of deploying large, computationally expensive models, impacting both the efficiency of research and the accessibility of powerful AI applications.