Integer Quantization: The Engineering Behind Fitting LLMs on Consumer Hardware

Integer quantization has evolved from a niche technique to a critical capability for deploying large language models efficiently, reducing memory footprint by 4x and energy consumption dramatically compared to floating-point operations. This comprehensive guide addresses the fragmentation in existing resources by building core concepts from first principles, essential for practitioners navigating the trade-offs between model performance and computational constraints. Understanding these foundations is increasingly vital as organizations seek to deploy 70B+ parameter models on consumer-grade hardware.