A Visual Guide to LLM Quantization

A Visual Guide to Quantization offers an in-depth look into quantization techniques for compressing Large Language Models (LLMs), including GPTQ, GGUF, and BitNet methods, to make them suitable for consumer hardware without compromising performance.

• LLMs often exceed billions of parameters.
• Quantization compresses model size.
• GPTQ focuses on GPU efficiency.
• BitNet reaches 1.58-bit models.
• QAT includes quantization in training.