Optimizing Local Inference: A Practical Guide to Running Small Language Models on WebGPU
Introduction The rapid democratization of large language models (LLMs) has sparked a new wave of interest in local inference—running models directly on a user’s device rather than relying on remote APIs. While cloud‑based inference offers virtually unlimited compute, it introduces latency, privacy concerns, and recurring costs. For many web‑centric applications—interactive chat widgets, code assistants embedded in IDEs, or offline documentation tools—running a small language model entirely in the browser is an attractive alternative. ...