Quantization for Small Models: A Practical, Reproducible Guide
Quantization for Small Models: A Practical, Reproducible Guide This article outlines a clear, reproducible workflow for applying quantization to small language models. The objective is to reduce memory usage, improve inference efficiency, and retain acceptable accuracy on constrained hardware. Purpose Quantization converts model weights from floating-point formats (fp32 or fp16) into lower-precision representations such as int8 or int4. This reduces VRAM and RAM consumption and enables running larger models on limited devices without modifying model architecture. Scientific Basis Quantization reduces the numeric precision of weights while preserving structural relationships. 4-bit methods apply additional techniques (double quantization, grouped quantization) to minimize accuracy loss. Inference is feasible because many transformer components are resilient to reduced precision. When to Use Quantization Scenario Suitability Running models on 4–8 GB GPUs Highly s...