Detailed Guide to Quantisation Methods for LLMs
Last Updated on September 12, 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.
The article explains quantisation techniques used in machine learning, emphasizing the significance of Quantisation Aware Training (QAT) and Post-Training Quantisation (PTQ). It discusses various methods for quantising large language models, offering insights into how these approaches can reduce model size and improve efficiency, along with a detailed breakdown of different quantisation strategies and their applications, as well as the file formats used for storage and deployment of quantised models.
Read the full blog for free on Medium.
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