Optimizing Small Language Models for Local Edge Inference: A Guide to Quantization in 2026
Introduction The past few years have witnessed an explosion of small language models (SLMs)—architectures ranging from 7 M to 300 M parameters that can run on modest hardware while still delivering useful conversational or generation capabilities. By 2026, these models are no longer experimental curiosities; they power everything from voice assistants on smart speakers to on‑device summarizers in mobile apps. Running an SLM locally (i.e., edge inference) offers several compelling advantages: ...