If an AI model runs on your device with its network connection disabled, its inference request is processed on that device—not sent to a remote model service. But “local” describes where the model computes, not everything an app may read, save, or transmit. To know where your data goes, check the full path: what context the app collects, where inference runs, and what happens to prompts, outputs, logs, and tool calls afterward.
What “off-grid AI” can mean
People use “off-grid” to describe several different setups. They are not equivalent for privacy:
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| Setup | Where inference happens | What connectivity means |
|---|---|---|
| Offline, on-device AI | On the device you are using | The app and model may need internet for initial downloads or updates, but inference can run with the device disconnected. |
| Local-network AI | On another computer on your home or office network | Your prompt travels to that machine over the local network. That is not the same as processing on the device in your hands, and the setup may or may not also have internet access. |
| Privacy-oriented cloud AI | On remote servers | Your request requires a connection. A provider may make privacy commitments about handling or retaining data, but the processing is still cloud-based. |
The distinction matters: avoiding a routine transfer to a cloud model provider is one meaningful privacy benefit of offline inference, but it does not establish that the app has no other access to your data or makes no network connections.
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1. Context the app can access
An AI app may use more than the words you type. Depending on its permissions and features, it could be given files, selected text, app content, notifications, or other device context. Check what you have authorized and what the feature says it will include. Running the model locally does not make that context inaccessible to the app itself.
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2. The prompt and attachments
When you submit a request, identify everything that goes with it: the prompt, attached files, retrieved passages, and any context assembled by an agent. In a truly offline setup, that request can be passed to a model on the same device without going to an internet-hosted inference service. On a local-network setup, it is sent to the computer doing the inference.
3. The inference destination
Inference is the model’s processing of a request to produce a response. The destination can be the device, another machine on your local network, or a remote cloud server. Some products choose between destinations depending on the task, so a product that often works on-device should not automatically be treated as exclusively offline.
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4. What happens around and after the response
Prompts and answers may be saved locally in chat history, caches, logs, or other app data; whether they are, and for how long, depends on the product and settings. An app may also make separate connections for tools, search, telemetry, crash reports, or updates. An agent’s tool call can have its own destination even when the model that chose the tool is local. Check these behaviors separately; they are not settled by the model’s location.
Examples: local inference, local-network inference, and hybrid AI
NVIDIA PAIR: a local-network example
NVIDIA describes PAIR as a beta that routes inference among compatible Windows systems with RTX GPUs, DGX Spark, and Macs, with Ollama and LM Studio supported at launch. Its product documentation says prompts, files, and agent context stay on the user’s local network. That is NVIDIA’s description of PAIR, not an independent audit of every connected third-party app or a rule that applies to all local-AI software.
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NVIDIA says PAIR does not require internet to operate, although internet is required to download models. Its listed requirements—8 GB RAM or higher and at least 20 GB of recommended disk space—are PAIR-specific, not minimum requirements for local AI generally. This illustrates a common distinction: setup can be online while subsequent inference is offline or confined to a local network.
Apple Intelligence: on-device when possible, cloud for some requests
Apple documents Apple Intelligence as a hybrid system: it processes tasks on-device when possible and uses Private Cloud Compute (PCC) for some more demanding requests. Apple says PCC uses personal data to fulfill a request and does not retain it after the response; Apple’s transparency reporting can show requests processed by PCC. These are Apple’s documented design and privacy commitments, not the same guarantee as a device with its network disconnected.
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Apple’s WWDC26 developer-session transcript makes the distinction explicit: “They both offer privacy. But the on-device model works offline, while PCC requires an internet connection.” In that comparison, “they” refers to Apple’s on-device model and PCC. The session describes the offline model as having a 4K context and no request limits, while PCC has a 32K context, a daily per-user limit, and reasoning support. Those are specifications discussed for Apple’s models in that 2026 session, not general limits for offline and cloud AI.
In September 2026, Apple described two on-device foundation models and three server-based models on PCC. Apple said its Cloud Pro model uses NVIDIA GPUs in Google Cloud while retaining Apple’s stated PCC privacy commitments. This is an example of why infrastructure, processing location, and a provider’s privacy promises should be considered separately: a service can use third-party data-center infrastructure and still describe a privacy boundary, but it is not offline processing.
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How to assess a specific AI setup
Before entering sensitive information, check the app, its settings, and the model destination—not just the model name. These questions help distinguish a genuinely offline workflow from one that is local only at the inference stage:
- What can the app read? Review permissions and the app’s explanation of file, app, notification, or other context access. Limit access to what the task needs.
- Where does this request run? Look for routing or cloud-fallback settings, and whether they apply to all requests or only certain tasks.
- What is retained? Check whether chat history, prompts, outputs, logs, caches, embeddings, or summaries are saved, where they are stored, and whether you can delete them.
- What can tools connect to? Search, browsing, connectors, and other agent tools can make their own network calls. A local model does not make those calls offline.
- What background traffic is enabled? Review telemetry, crash reporting, updates, and related privacy controls. Their behavior varies by app, version, and configuration.
- Can you verify the route? Prefer products that make routing and privacy controls inspectable. If the distinction matters for your use, you can test network behavior or block internet access—but blocking connections may also disable features or prevent downloads and updates.
A 2026 preprint on local-AI governance treats context access, persistence, tool authority, telemetry, and lifecycle controls as separate questions. It offers a framework for evaluating systems; it is not an empirical audit of every local-AI app. There is no universal inventory of what all local runners save or transmit, so product-specific documentation and configuration matter.
What offline inference does—and does not—protect
Disconnecting the network after downloading the app and model can prevent a routine inference request from reaching a remote model service. That is the clearest way to establish that the request is not being sent out during inference. It cannot undo information already uploaded during setup, and it does not by itself tell you what the app stores on the device. Nor does it cover another device on your local network if your setup sends requests there.
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Local processing can also involve practical trade-offs. Models, available context, reasoning features, request limits, and hardware needs vary by product. Use dated, product-specific specifications rather than assuming that every offline model has the same capabilities or requirements as a cloud service. For example, the 4K and 32K context figures above belong to Apple’s WWDC26 model comparison; NVIDIA’s RAM and storage figures apply to PAIR.
The useful privacy question is therefore not simply “Is this AI local?” It is: which device or server receives each part of my request, what context can the app access, what is retained, and which features can connect elsewhere?
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