From ML to LLM: on-device AI in the browser by Nico Martin

Modern browsers now support on-device AI/ML through WebGPU API, WebAssembly, and TensorFlow.js backends for accelerated neural network processing

Running LLMs in browser requires handling large model sizes (1.4+ GB) but enables privacy-preserving, offline-capable AI features with no server costs

WebNN API proposal aims to provide standardized access to AI-optimized hardware (TPU, NPU) across different devices and browsers

RAG (Retrieval Augmented Generation) can be implemented entirely client-side to ground LLM responses in local documents and prevent hallucination

Real-time tasks like speech recognition, image detection achieve 30+ FPS through WebGPU acceleration vs 5 FPS on CPU

Quantization reduces model size by using 4-bit precision instead of 32-bit, making browser deployment more feasible

Progressive enhancement approach recommended - AI features should enhance core functionality rather than being required

Models and weights can be cached locally after initial download for improved performance

Open source tools like Transformers.js, ONNX Runtime Web, and TensorFlow.js enable browser-based ML development

On-device AI allows building privacy-preserving applications that work offline without sending sensitive data to cloud providers