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What happens after you enter a prompt?
The app or system feature you invoked determines how the request is handled. It may run inference on the phone, send a request to a cloud service, or route different requests through different paths. Inference is the part of the process where a model uses the input to produce an answer.
- Local inference: A supported model runs on the phone’s hardware and returns its result there. For supported tasks, this can work without a network connection.
- Cloud inference: The device sends a request over a network to remote computing infrastructure, which processes it and returns a response. What is sent and how it is handled depend on the service.
- Hybrid routing: A product can use an on-device model for some requests and a cloud model for others. The software may choose the route based on the feature or request; users do not necessarily see or control that choice.
These routes describe where the model handles a particular task. They do not establish that every surrounding activity—such as account access, synchronization, retrieval, or telemetry—also happens locally.
How do Apple Intelligence and Gemini Nano illustrate the difference?
Apple Intelligence: on-device processing and Private Cloud Compute
Apple says many Apple Intelligence tasks run entirely on device, while more complex requests that need additional computational capacity can use Private Cloud Compute (PCC). Apple’s architecture documentation describes a PCC request as containing the prompt, the desired model, and inference parameters. Apple says PCC processes only data relevant to the request and removes it afterward; its security guide sets out a requirement that user data not remain accessible after a response. These are Apple’s descriptions and design requirements, not an independent audit of every request.
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Apple’s privacy documentation gives a concrete example: when a user creates an image with Image Playground, the device sends the description to PCC for a server-based model. So even within one company’s AI features, the path can differ by task.
Apple’s support page lists Apple Intelligence for iPhone 15 Pro, iPhone 15 Pro Max, and iPhone 16 models or later. Eligibility and availability can change and may also depend on software, language, and region; check Apple’s current Apple Intelligence support information for the feature and device you plan to use.
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Android: Gemini Nano through AICore
Google says Gemini Nano can provide generative AI experiences without a network connection or sending data to the cloud. On Android, Gemini Nano runs through AICore, a system service that uses device hardware and supports low-latency inference; Google’s ML Kit GenAI APIs are built on AICore to facilitate on-device execution. See Google’s Gemini Nano documentation.
This describes a supported on-device path, not a guarantee that every Gemini-branded feature, every assistant request, or every Android phone uses Gemini Nano. Confirm that the particular phone and feature support it.
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Does your phone send your AI prompt to the cloud?
It can, but the product name alone does not answer the question. For a specific request, check the feature’s documentation for its processing route and the provider’s explanation of what information is transmitted, retained, or deleted. A feature may also combine local inference with network-dependent operations that are separate from generating the answer.
Apple offers a platform-specific way to inspect some routing: its privacy documentation says users can turn on transparency logging and review an Apple Intelligence & PCC Report in device settings. This is an Apple facility, not a general Android control. Apple also explains the logging option and report in its Apple Intelligence privacy documentation.
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What changes when processing is local or remote?
- Connectivity: A supported on-device task can work offline; a cloud request needs a network connection.
- Network round trip: Local inference avoids sending the prompt to a remote model and waiting for its response over the network. That does not establish that it will always feel faster: performance depends on the task, device, and service.
- Computing capacity: A cloud route can use a different or more computationally capable system. Whether that produces a better answer for a given task depends on the models and request.
- Privacy scope: Local inference means the model handles that task on the phone. It does not by itself prove that the app makes no network requests or that all associated data stays local. For cloud processing, consult the provider’s stated data-handling policy and architecture.
- Reasoning and latency: Apple’s developer documentation says lower reasoning effort reduces latency, while deeper reasoning trades latency for more analysis. This describes a configurable trade-off in its developer framework, not a comparative phone benchmark.
The available platform documentation does not provide comparable current measurements to rank phones or platforms on speed, output quality, battery use, or heat. Those results should not be inferred from “on-device” or “cloud” labels alone.
How to check the route for a feature you use
- Name the exact feature. Distinguish, for example, an image-generation tool from a writing or assistant feature; one brand can offer different processing paths.
- Check support for your phone and software. Use the provider’s current compatibility information, including any language or regional limits. A feature’s existence on one model does not establish support on another.
- Look for processing and connectivity details. Check whether the documentation says the named task runs on device, uses a cloud service, or may use either route. If it only describes one path, do not assume every other task follows it.
- Review the provider’s data-handling explanation. Separate the prompt and model request from other app activity, and look for stated retention, deletion, controls, or transparency reports.
- Test offline only as a practical check. If a feature works with connectivity disabled, that is useful evidence for that use at that time; it is not proof that every feature in the app is local or that no data is sent when the phone reconnects.
What to check before choosing a phone for on-device AI
Do not treat an “AI phone” label as a promise that every prompt stays on the device. Verify the exact handset, operating-system version, feature, and regional or language availability. Apple’s cited compatibility page covers the listed iPhone models; Google’s Gemini Nano documentation describes Android capabilities but does not establish that all Android phones support them. If local processing is important, also check the feature’s offline behavior and the maker’s explanation of any cloud fallback.
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