Giving a small home AI an offline Wikipedia made it far more accurate

A small AI model running on an old desktop computer answered far more general knowledge questions correctly once it was given an offline copy of Wikipedia. In a test by How-To Geek writer Adam Davidson, it went from 4 right out of 13 to 11 right, plus one partly right. None of its answers contained made-up facts.

What the test showed

The computer was a Dell OptiPlex more than a decade old, with 16 GB of RAM and no dedicated graphics card. The model was Qwen 3.5 4B, run through a free tool called Ollama. On its own, it often gave confident answers that were simply false.

• It said Iain Sinclair wrote The Wasp Factory in 1983. The real answer is Iain Banks in 1984.
• It named the wrong founder of Home Assistant. The correct name is Paulus Schoutsen.
• With Wikipedia, 11 answers were right and one was partly right.
• It declined one question, about the 2026 Six Nations rugby winner, because it believed the year was 2024.

How the setup works

Small models cannot hold much knowledge, and they tend to guess rather than admit they do not know. The fix is to keep the facts outside the model. Kiwix is a free app that can serve offline websites from your own computer. Kiwix Wiki MCP Server connects it to the AI, so the model can look things up when it needs to.

The library is stored in compressed files called ZIM files. The test used a text-only copy of English Wikipedia, about 50 GB. Other files are available too, including Project Gutenberg libraries on subjects such as law and technology, iFixit repair guides, and Stack Exchange data. The model does not store the information. It pulls what it needs from the server.

The trade-off

Accuracy cost time. Answers took around two minutes, compared with about nine seconds without Wikipedia. On older hardware, that is a real wait. The author still judged the output far more accurate, since every response used real data pulled from the library.

Why it matters

Cloud AI services process your questions on someone else's servers. Running a model at home keeps that private, but stronger models need expensive hardware, and the article notes that RAM prices have jumped. This approach offers a cheaper path: a small model with a big reference library. It is one writer's test with 13 questions, so results will vary, but the gap was wide.