<ul data-eligibleForWebStory="true"><li>Quantization is a method used to enable the use of large language models (LLMs) on limited hardware by reducing the number of bits per parameter.</li><li>Most LLMs currently utilize 16-bit floating point formats, but using 4-bit quantization can lead to up to a 4x reduction in model size.</li><li>Quantization algorithms are designed to minimize model size while maintaining performance.</li><li>This article delves into the Activation-Aware Weight Quantization (AWQ) algorithm and its application to a small local LLM in a practical scenario.</li><li>Quantization involves reducing the number of bits required to represent variables, resulting in smaller data storage.</li><li>An example demonstrates the process of converting a 16-bit floating-point number to a 4-bit representation through a scaling factor.</li></ul>

How to Use AWQ to Quantize LLMs

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