# Quantization Aware Training

> Source: https://sukruyusufkaya.com/en/glossary/quantization-aware-training
> Updated: 2026-05-13T19:59:43.759Z
> Type: glossary
> Category: uretken-yapay-zeka-ve-llm
**TLDR:** An approach that trains the model under low-precision conditions to preserve quality after quantization.

<p>QAT aims to reduce later quality loss by exposing the model to quantization effects during training itself. Although more costly than PTQ, it can preserve accuracy better in some tasks. It is especially valuable in production settings with strict hardware constraints.</p>