Quantization Is a Silent Fine-Tune Breaking Your Edge AI
dev.to
TL;DR — Quantizing a small language model for on-device deployment doesn't just shrink it — it changes its behavior in ways that generic benchmarks don't catch. Teams treat quantization as a lossless export step, then discover in production that tool-calling, numeric reasoning, or formatting compliance quietly broke. Quantization needs the same task-specific regression testing as fine-tuning, and the real deployable unit is the model+quantization+runtime+hardware tuple, not the checkpoint alone