Скачать или смотреть How to Run an LLM Locally in Your Browser with WebAssembly (Gemma Web Demo)

What if a multi-billion parameter AI could run in a single browser tab, completely offline? We built it.

🚀 Explore the architecture in action: gemma-web-ai.vercel.app

This project was a deep dive into the limits of client-side computing. My collaborator, [Tag Your Co-Developer], and I set out to prove that a powerful, private AI experience could be engineered without servers.

Here are the core technical achievements:

On-Device Inference Engine: We engineered a local-first engine using @Google's MediaPipe LLM Task API, enabling Google's Gemma models to run entirely in the browser via WebAssembly for a zero-latency, serverless experience.

Client-Side RAG Pipeline: I implemented a complete Retrieval-Augmented Generation pipeline, using a Web Worker to process user-uploaded documents (PDF, TXT) into vector embeddings with @TensorFlow.js without blocking the main UI thread.

Robust Local Storage: I architected a comprehensive client-side storage solution using IndexedDB to efficiently cache large AI models, manage conversation histories, and persist document embeddings for full offline functionality.

This robust backend powers a feature-rich UI with voice-to-text, text-to-speech, and an incognito mode.

I’m passionate about solving hard engineering problems. Let’s connect and discuss the future of web-native applications.

#WebAssembly #OnDeviceAI #RAG #TensorFlow #IndexedDB #Engineering #Gemma #Privacy #LLM