Deploying Edge AI Inference for Privacy-First User Experiences

Running quantized transformer models directly in the browser using WebGPU eliminates the need to send user data to inference servers, fundamentally changing the privacy calculus for AI-powered features. The implementation involves model quantization to 4-bit precision, WASM-based tokenization, and progressive model loading that keeps initial page weight under 2MB. Early benchmarks show acceptable latency for classification and summarization tasks on devices from the last three hardware generations, making this viable for production deployment today.