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Run large language models directly in the browser using WebGPU and WASM
Build a groundbreaking inference engine that runs LLMs entirely in the browser without server calls. Uses WebGPU for GPU acceleration, WASM for performance, and quantization techniques to reduce model size. Features streaming generation, temperature control, and system prompt customization. Includes model caching, offline support, and privacy-first architecture where user data never leaves the device.
WebGPU acceleration
4-bit/8-bit quantization
Streaming generation
Prompt templates
Model caching
Offline support
System prompt management
Conversation history
Export/import chats
Privacy-first design
Client-side only architecture with WebGPU compute shaders, WASM modules for tokenization, IndexedDB for model storage, Service Workers for offline support, and Web Workers for non-blocking inference
WebGPU/WebGL
WebAssembly
TypeScript
ML model optimization
Browser APIs
Performance profiling
Master WebGPU compute
Optimize models for browser
Build offline-first apps
Implement streaming responses
Design privacy-focused AI
Profile browser performance
WebGPU capabilities and model quantization
WebGPU compute pipeline
Tokenizer implementation in WASM
Basic inference engine
4-bit quantization implementation
Token streaming with SSE
IndexedDB model caching
Chat interface with markdown
Service Worker integration
Benchmarking and optimization