Optimizing Local Inference: How SLMs are Redefining the Edge Computing Stack in 2026
Introduction In 2026 the edge is no longer a peripheral afterthought in the artificial‑intelligence ecosystem—it is the primary execution venue for a growing class of Small Language Models (SLMs). These models, typically ranging from 10 M to 500 M parameters, are deliberately engineered to run on resource‑constrained devices such as micro‑controllers, smart cameras, industrial IoT gateways, and even consumer‑grade smartphones. The shift toward on‑device inference is driven by three converging forces: ...