---
title: "Discussion on \"LLM Model Quantization: Why, When, and How to Deploy Smaller, Faster Models\" | Hashnode"
description: "Discussion on \"LLM Model Quantization: Why, When, and How to Deploy Smaller, Faster Models\". Cut GPU memory and latency by converting FP16 weights to INT8 or INT4 — without retraining from scratch."
url: "https://hashnode.com/posts/llm-model-quantization-why-when-and-how-to-deploy-smaller-faster-models/69add67686766ac3a6547f41"
---

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# [LLM Model Quantization: Why, When, and How to Deploy Smaller, Faster Models](https://abstractalgorithms.hashnode.dev/llm-model-quantization-why-when-and-how-to-deploy-smaller-faster-models?utm_source=hashnode&utm_medium=feed)

Cut GPU memory and latency by converting FP16 weights to INT8 or INT4 — without retraining from scratch.

Mar 8 · 13 min read · abstractalgorithms.hashnode.dev  
Updated Jun 18  
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