A developer set out to answer a stubborn question: can local LLMs be trusted to answer technical questions without leaning on the cloud? The experiment was systematic. Starting from the official documentation of projects like Node, LangChain.js, TypeScript, Transformers.js, and Vue, the developer used DeepSeek-v4-flash to generate multiple-choice questions and then benchmarked local Unsloth Gemma QAT models under three conditions: no context, the correct document injected manually (oracle), and a fully automatic RAG system that retrieves the most relevant documents without preselecting the right one.

The outcome is stark. Without RAG, the models stumble; accuracy remains too low for professional use. Inject the correct document, and performance jumps to impressive levels. Even the full RAG setup — which must guess the right documents — proves to be a reliable path to making an on-premise LLM genuinely useful. Thinking mode, often touted as an edge, barely moves the needle: roughly one percentage point improvement against much longer inference times.

The most striking figure involves Apple Intelligence, a small model embedded in Apple devices. Its context window is limited to roughly 4K tokens, far less than the 32K granted to the other models, so the RAG system was set to inject only the top three documents instead of five. Despite this squeeze, Apple Intelligence scored 86% accuracy — a remarkable outcome for a model running entirely on-device.

What this means for on-premise decisions

This isn't just a tinkerer's curiosity. It touches a strategic line: the boundary between cloud API dependency and local autonomy. A local LLM, even a modestly sized one, paired with a corporate knowledge base and a well-tuned RAG pipeline can handle technical questions with reliability approaching cloud solutions, but with full data control. For regulated industries where data residency is non-negotiable, this combination offers a third way — avoiding both risky do-it-yourself setups and lock-in to big providers.

The trade-off is context management. The model's attention window remains a bottleneck: long documents and aggressive chunk injection can overflow it, forcing compromises. Apple Intelligence shows that a tiny model can outperform expectations when the RAG is carefully designed. The structural message is that the race toward ever-larger models may not be the only path. The local ecosystem is maturing to the point where for many use cases, investment shifts from monster GPUs to smarter integration between retrieval and generation.

Building an effective RAG system is not plug-and-play. It requires tuning, relevance evaluation, and a robust indexing pipeline. But the numbers from this benchmark show the effort pays off. They also signal that the future of local inference is not just about models, but about hybrid architectures where external knowledge fills the capability gap of any single model.