Built an SQLite-based memory system for Ollama that doesn't eat all your VRAM

Wanted to share a project I've been working on to give local Ollama models persistent document memory without needing heavy vector databases. It's called Hillock. The main problem I had with standard local RAG was that running document parsing and vector search chewed up so much VRAM that my actual Ollama model (like Llama 3 8B or Qwen) ran painfully slow. With this: You drop in a doc, and it extracts facts in ~5 seconds using tiny sub-300MB models instead of an LLM. Facts go into an SQLite database. When you ask a question, it checks if the knowledge actually exists first using hypervector math. If you ask something outside your notes, it stops the model from hallucinating. It has a /model command in the CLI that automatically detects whatever Ollama models you have pulled on your machine and lets you swap between them, plus an OpenAI-compatible API if you prefer using Open-WebUI. Fits in under 1.2GB VRAM (or runs on CPU). Repo is open source: https://github.com/roandejager/Hillock submitted by /u/Equivalent-Flan-1590 [link] [comments]

Related

• Built a local memory engine for Ollama that runs in <1.2GB VRAM without vector DBs (Hillock v0.5)
• Running a local model as a SQL assistant against real databases: notes on what breaks

Source: r/ollama | 2026-10-01