Local LLM Inference for Privacy-Preserving Browser Intelligence—Open Architecture and Optimization

Short summary

Kathon Local AI Engine enables private LLM and vision-language model inference entirely in-browser using llama.cpp and Qwen 2.5 VL, with Rust backend and WebSocket frontend—eliminating cloud dependency and data extraction. Open-source architecture uses speculative decoding, KV-cache quantization, and prompt caching for optimization. Built by 23-year-old Lois-Kleinner Alpasan as alternative to cloud-based AI requiring remote data transmission.

• •Local-first inference engine runs LLMs entirely on-device via llama.cpp framework, no cloud required
• •Specific stack: Rust server, React/TypeScript frontend, Qwen 2.5 VL Q4 quantized model with optimizations
• •Open-source, auditable, immutable ledger tracking; privacy by architecture, not policy

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