The paper addresses the challenge of balancing accuracy and efficiency in large language models (LLMs) by exploring quantization techniques. Specifically, it focuses on reducing the precision of model parameters to smaller bit sizes while maintaining performance on zero-shot tasks. The research highlights the importance of selecting 4-bit precision, along with strategies like quantile quantization and floating-point representation, to optimize memory footprint and speed of inference in LLMs. Engineers and specialists can leverage 4-bit precision quantization with techniques such as quantile quantization and floating-point representation to significantly reduce the memory footprint and improve inference speed of large language models. Understanding the trade-off between accuracy and efficiency is crucial for deploying powerful NLP technologies in resource-constrained environments and expanding their applications to real-world scenarios. Read full paper: https://arxiv.org/abs/2212.09720 Tags: Machine Learning, Natural Language Processing, Quantization, Efficiency, Model Compression