Beginner’s Visual Guide to Quantisation Methods for LLMs
Last Updated on September 14, 2025 by Editorial Team
Author(s): Parth Chokhra
Originally published on Towards AI.
A Visual Step-by-Step Guide to Popular Quantisation Techniques
Quantisation is the process of reducing the precision of numbers used in a model; for example, storing weights in 8-bit integers instead of 16- or 32-bit floats. This makes models smaller, faster, and more efficient to run, often with only a small drop in accuracy. For large language models, quantisation is especially important because of their size and hardware demands. To understand this, look at the example below.
This article explores various quantisation methods for large language models, focusing on two main approaches: Quantisation Aware Training (QAT) and Post-Training Quantisation (PTQ). The author discusses different algorithms and techniques associated with these methods, highlighting their trade-offs in accuracy and speed. The article also addresses the implications of these methods for deployment and provides insights on how to choose the best approach based on specific use cases.
Read the full blog for free on Medium.
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