What Is an Air-Gapped Local AI Setup?
An air-gapped local AI setup is a network with no internet connection at all, where every component, including the language model, runs inside that network, so nothing is downloaded, checked, or sent out during use. The defining test is simple: disconnect the network, and a true offline model keeps answering, while a setup that still needs a download, a license check, or a cloud call does not qualify.
Updated November 2, 2026. This page is general information, not legal advice.
Air-gapped versus merely on-premises
On-premises AI runs on infrastructure you control, but it may still reach the internet for models, updates, or licensing. Air-gapped AI runs on a network with no internet access at all, so every component has to run inside it. That distinction decides the whole design: an on-premises setup can pull a model on first run and check a license online, while an air-gapped setup must have its models, tools, fonts, scripts, and supporting services already inside the boundary before the network is closed.
The checklist before calling a deployment air-gapped is therefore a search for hidden phone-home behaviour. The language model must run locally, embeddings and search for any knowledge base must run locally, no telemetry, crash reports, or analytics may be sent out, licensing must work offline, updates must arrive on a manual channel and be verified before install, agent tools must point only at systems inside the network, and every asset in the admin application must be served locally. Miss any one of those, and the deployment is private in spirit but not air-gapped in fact.
How an offline install actually works
An offline install means downloading the runtime and the model files, in GGUF format, on an internet-connected machine, then transferring those files safely to the target machine that has no internet access. Once installed, the local runner serves models entirely on that hardware, with no outbound connections required. The transfer itself can use an internal local network, a one-way transfer method, or a container image built with the runtime and model files baked in, so the container needs no internet access after build time.
The practical sequence is to prepare everything while connected, verify the model runs on the target hardware, and then prove offline operation by disconnecting and asking a question. Small open-weight models can run comfortably offline on ordinary hardware, while larger models need substantially more memory, and the exact sizing for a particular model is unknown on this page until you check that model's requirements. Test tool use, long-instruction following, and response speed on your own hardware before committing, because a model that answers well in a demo may call tools unreliably or follow long instructions poorly in production.
What you give up, and what you gain
An offline model cannot browse the web, use live data, or call external tools unless you intentionally add those features back inside the boundary, and its knowledge stops at its training cutoff. That is a real cost for tasks that need current information, and it should be weighed honestly against the privacy gain. For classified, critical-infrastructure, or strictly regulated work, the gain is decisive: prompts, documents, and outputs never cross the security boundary, and no vendor can log, retain, or train on them.
For ordinary private work, the same test applies at smaller scale. Download the model while connected, disconnect the device, and confirm offline operation by asking a question with the network switched off. If it answers, and if no part of the pipeline phones home for telemetry, updates, or licensing, you have a genuinely offline setup. If any part fails without a connection, that part is the gap to close, either by carrying it inside the boundary or by replacing it with a local alternative.