Build a 100% offline fast Retrieval Augmented Generation (RAG) system that runs without an internet connection, without cloud APIs, without OpenAI/Ollama
Local RAG isn’t solved by just replacing OpenAI with a local open weights model. Document processing, embeddings, vector search, and inference all become part of the optimization problem.
I tried Google LiteRT-LM GPU inference (+ streaming) with Qdrant Edge and compared it against the core Qdrant Client + Ollama pipeline. The LiteRT-based setup delivers lower end-to-end inference latency while keeping everything fully local (macOS-arm64). The blueprint results are available in the repository.
Published a video for the same:
covers an end-to-end offline RAG pipeline using EdgeParse, Qwen 3 Embeddings, Qdrant Edge, and Gemma 4 LiteRT-LM
Including Hybrid search, streaming inference, TurboQuant, and tradeoff considerations for on-device applications.
> Along with UI built using Streamlit running without internet (local)
Tutorial: