Privacy First: Run Your Own Health Assistant LLM Entirely in the Browser (No Backend Required!) A developer built a private health consultation bot that runs entirely in the browser using WebLLM and WebGPU, eliminating the need for backend servers and ensuring zero data leakage. The system executes a quantized Llama-3-8B model locally via TVM.js and Web Workers, with model weights cached in IndexedDB. Have you ever wondered why your most personal health queries need to travel across the globe to a centralized server just to get a simple answer? In an era where privacy-preserving AI is becoming a necessity rather than a luxury, the paradigm of Edge AI is shifting the landscape. By leveraging WebLLM and the raw power of WebGPU , we can now execute high-performance Large Language Models LLMs directly within the browser sandbox. No API keys, no server costs, and most importantly—zero data leakage. Today, we are building a private health consultation bot that runs 100% client-side. Before we dive into the code, let’s talk about why this matters. Traditional AI architectures rely on heavy GPU clusters. However, with the advent of the WebGPU API, we can tap into the user's local hardware. This approach offers: If you are interested in more production-ready examples and advanced architectural patterns for decentralized AI, I highly recommend checking out the deep dives over at WellAlly Tech Blog https://www.wellally.tech/blog . To make this work, we use TVM Apache TVM as the compilation stack, which allows models to run on different backends, and WebLLM as the high-level interface for the browser. php graph TD A User Input -- B React Frontend B -- C WebLLM Worker C -- D{WebGPU Support?} D -- Yes -- E TVM.js Runtime D -- No -- F Fallback/Error E -- G IndexedDB Model Cache G -- H Local GPU Inference H -- I Streamed Response I -- B To follow this tutorial, ensure you have: tech stack : First, we need to initialize the MLCEngine . Since LLMs are heavy, we should run the inference engine inside a Web Worker to keep the UI thread buttery smooth. 🚀 js // engine.ts import { CreateMLCEngine, MLCEngine } from "@mlc-ai/web-llm"; const modelId = "Llama-3-8B-Instruct-q4f16 1-MLC"; // Quantized for browser use export async function initializeEngine onProgress: p: number = void { const engine = await CreateMLCEngine modelId, { initProgressCallback: report = { onProgress Math.round report.progress 100 ; }, } ; return engine; } We want a clean way to interact with our local model. Let's wrap the logic into a custom hook. js // useHealthAI.ts import { useState } from 'react'; import { initializeEngine } from './engine'; export const useHealthAI = = { const engine, setEngine = useState