The Shift to Local-First AI: Optimizing Small Language Models for Browser-Based Edge Computing
Introduction Artificial intelligence has traditionally been a cloud‑centric discipline. Massive language models (LLMs) such as GPT‑4, Claude, or Gemini are trained on huge clusters and served from data‑center APIs. While this architecture delivers raw power, it also introduces latency, bandwidth costs, and—perhaps most critically—privacy concerns. A growing counter‑movement, often called Local‑First AI, proposes that intelligent capabilities should be moved as close to the user as possible. In the context of web applications, this means running small language models (SLMs) directly inside the browser, leveraging edge hardware (CPU, GPU, and specialized accelerators) via WebAssembly (Wasm), WebGPU, and other emerging web standards. ...