To find out whether an AI feature runs on your PC or in the cloud, check documentation for the exact feature and software version, then look for a request-level report that identifies where a specific task was processed. An internet connection, network activity, an NPU, or a broad privacy claim cannot prove by itself where an individual request ran.
Start by identifying the exact feature
“The assistant” or “the AI in this app” may combine several features that use different processing paths. Record the app or operating-system feature, device model, app and OS versions, relevant region, and the action you want to check—for example, summarizing a note rather than simply opening the assistant.
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Keep the question narrow: did this particular action run locally, go to a server, or use a mix of both? A device’s ability to run local models does not establish that a particular app or request uses them.
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Check feature-specific documentation first
Look for explicit language such as “on-device,” “local inference,” “server-based,” “cloud model,” “requires a network connection,” or “works offline.” Check that the statement applies to the specific task, not just the product family or the platform’s general capabilities. If the vendor describes a hybrid approach or fallback, note that different requests may take different routes.
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Prefer product documentation over a hardware label or general privacy marketing. If the documentation does not say where the named task runs, treat its execution location as unverified unless a request-level report can establish it.
Look for a report about the individual request
A request-level report that names the execution environment is stronger evidence than an overall network monitor. On supported Apple platforms, the Apple Intelligence & PCC Report records requests handled on-device and those sent to Private Cloud Compute (PCC). To check a task, find the report under Settings or System Settings > Privacy & Security > Apple Intelligence & PCC Report, perform the task, then export and inspect the report. The exact label and export instructions can vary by OS version; Apple’s privacy page and report appendix explain the feature and report fields, including the `executionEnvironment` values `OnDevice` and `PrivateCloudCompute`. Apple notes that the report format changed beginning with OS versions 26.4, so follow the instructions for the installed release.
Apple says many Apple Intelligence tasks run on-device, while some requests may be sent to PCC when they require a model with more compute. Image Playground image generation is an example Apple identifies as a server-based model request. Apple says PCC processes data to fulfill a request and does not retain it after returning the result. That describes Apple’s stated handling; it does not mean every Apple Intelligence task uses the same route.
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Apple’s developer documentation also distinguishes its on-device Foundation Model from the server-based PrivateCloudComputeLanguageModel: the former works offline, while PCC requires a network connection and has a larger context window and a daily limit. These are API details, not proof of the route used by every consumer feature. See Apple’s developer guidance for the current comparison.
Use offline behavior as a clue, not a universal verdict
Disconnect the device from the network and repeat the same task only when doing so is safe and practical. If the task succeeds, that is evidence that this operation completed without a live network at that moment. It does not prove that every feature in the app always runs locally, or that the same operation will use the same route under different conditions.
If it fails, the cause may be a remote model, but it could also be authentication, model availability, a required download or update, or another online dependency. Offline tests are most useful when paired with documentation that says whether the specific task is expected to work without a connection.
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Do not treat network traffic as proof of cloud inference
A firewall or packet monitor can show that the PC connected to a destination and when. It usually cannot establish that a particular prompt was uploaded or where its model inference happened. A locally processed task may coincide with sign-in checks, sync, telemetry, search, content retrieval, or model downloads and updates. Conversely, encryption or delayed requests can make traffic difficult to interpret.
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Windows Recall
Microsoft says Recall snapshots are locally stored and analyzed on the PC, and states: “No internet or cloud connections are required or used to save and analyze snapshots.” That statement applies to Recall’s snapshots, not to every AI feature on Windows. Microsoft’s Recall management documentation lists minimum requirements of a 40 TOPS NPU, 16 GB RAM, eight logical processors, and 256 GB storage. It says at least 50 GB must be free to enable Recall and that automatic snapshot saving pauses below 25 GB free. These are Recall-specific requirements and storage behavior, not general requirements for local AI.
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A September 27, 2024 post by Microsoft executive David Weston described Recall as opt-in and said its snapshots and associated vector database are encrypted, with keys protected by TPM and tied to Windows Hello Enhanced Sign-in Security. That is a dated architecture statement; consult Microsoft’s current documentation for present behavior. See the Windows Experience Blog post.
Apple Intelligence and Private Cloud Compute
Apple provides a useful example of why “local or cloud” can have a request-level answer: many tasks run on-device, while some requests can go to PCC. Apple’s report distinguishes those environments, so check the report for the action in question rather than assuming a single route for all Apple Intelligence features. Apple’s privacy page describes the report under Settings/System Settings > Privacy & Security > Apple Intelligence & PCC Report; labels may vary by OS release. See Apple Intelligence & Privacy and the Apple Intelligence Report appendix.
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Google’s Android developer guidance distinguishes Gemini Nano, which runs on-device through AICore, from cloud-based Gemini Flash or Gemini Pro models used through Firebase. It also describes hybrid designs, where smaller tasks run locally and larger inputs or requests needing additional knowledge use cloud models. This is developer guidance; it does not establish the routing used by every third-party Android app. See Google’s Android AI/ML overview and Gemini Nano documentation.
AICore manages model delivery and updates, so network activity around an Android device does not automatically indicate that prompt inference was remote. Google’s end-user help gives this path to inspect the service: Settings > Google profile > All services > Privacy and security > System services > AICore. Feature availability varies by device and manufacturer. Google lists note summarization and smart replies among local examples in its AICore help page.
Local processing is only one part of privacy
“Local” describes where computation happens; it does not, by itself, explain what the app stores, which permissions it uses, whether data syncs to an account, what telemetry is collected, or whether extensions send information elsewhere. Consider those separately in the app’s privacy settings and documentation. Likewise, a retention claim about one service or request should not be generalized to other data the app handles.
Quick Recap
How to state what you found
- Verified for this request: use this only when feature-specific documentation or a request-level report identifies the execution environment.
- Works offline in this test: say that the named operation completed without a live network under the conditions tested; do not generalize to other features or times.
- Network activity observed: report the connection as a connection, not as proof that the prompt was uploaded or inference was remote.
- Location not verified: if there is no specific architecture statement or request-level evidence, say, “I could not verify where this request was processed.”
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