Quantization and Fine-Tuning in LLM: Cut Model Size by 75% Without Losing Accuracy
Last Updated on January 3, 2026 by Editorial Team
Author(s): Alok Choudhary
Originally published on Towards AI.
Transform massive AI models into lightweight versions. Discover how quantization makes LLMs accessible on mobile and edge devices.
In the world of Large Language Models (LLMs), two concepts are absolutely crucial for making these powerful AI systems practical and accessible: quantization and fine-tuning. Let me break down these concepts in simple terms so you can understand exactly what’s happening under the hood.
The article discusses the importance and applicability of quantization and fine-tuning in Large Language Models (LLMs), explaining how quantization compresses model size and makes them more accessible for practical use, particularly in mobile and edge devices. Key benefits such as faster processing, cost-effective fine-tuning, and efficient deployment in various applications are highlighted, alongside the trade-offs and challenges involved in maintaining accuracy during the quantization process.
Read the full blog for free on Medium.
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