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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesWhen you invoke an AI feature on a phone, the prompt can follow one of three paths: a model can process it on the phone, a remote service can process it in the cloud, or the product can choose between those paths. “AI on this phone” therefore does not mean that every prompt stays on the device. The route depends on the particular feature, model, device, software version, settings and request.
The three ways a phone can process a prompt
1. On-device inference
With on-device inference, the model executes on the phone’s hardware. The app or system feature passes the prompt to a local model, receives the result locally and can support the feature without a network connection when that capability is designed and enabled.
Google documents Gemini Nano running through Android’s AICore system service. Google says Gemini Nano can provide generative-AI experiences without a network connection or sending data to the cloud. Apple describes on-device processing as a cornerstone of Apple Intelligence and says many Apple Intelligence requests run entirely on the device.
2. Cloud inference
With cloud inference, the phone sends a request over a network to the provider’s compute environment and receives the result. In Apple’s documented Private Cloud Compute (PCC) architecture, that request contains the prompt, the desired model and inference parameters. This is Apple’s description of its own Apple Intelligence flow, not a universal format used by every AI service.
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3. Hybrid routing
A hybrid product can use a local model for some requests and a remote model for others. Apple says more complex Apple Intelligence requests that need additional computational capacity can use PCC, while other requests are handled on device. The product may make that routing decision automatically; users should not assume they can always see or override it.
What happens from tap to answer
- You invoke a feature. The starting point might be a system writing tool, an assistant, an image generator or an app’s own AI function.
- The feature evaluates its available models. Its design, device eligibility, operating-system version, settings and the request itself determine which inference path is available.
- The prompt is routed. A supported local model receives it on the phone, or the feature creates a network request for a remote model. In a hybrid design, the feature selects between those options.
- The model generates an output. Local inference returns the result on the device. A cloud request returns data over the network after remote processing.
- Other app operations may still occur. Even when inference is local, an app could separately perform account, synchronization, retrieval, telemetry or other network operations. Local inference does not prove that every surrounding operation is local.
Apple Intelligence: one product, two documented paths
On-device Apple Intelligence
Apple says many Apple Intelligence models run entirely on the device. For those requests, the model processing is local to the supported iPhone rather than being sent to Apple’s servers.
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Private Cloud Compute for harder requests
Apple says requests that require more computational capacity can use Private Cloud Compute, which runs on Apple silicon servers. Apple’s PCC security documentation says the system is intended for computationally intensive Apple Intelligence requests and requires that user data not remain accessible after a response returns. Apple also says that only data relevant to the request is processed before removal. These are Apple’s stated architecture and privacy requirements; they should not be generalized to every cloud AI service.
A concrete cloud example: Image Playground
Apple’s privacy documentation uses Image Playground as an example in which the device sends a description to Private Cloud Compute for a server-based model. That means an Apple AI feature can send prompt content to the cloud even though other Apple Intelligence requests may remain on the phone.
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Checking Apple’s transparency report
Apple documents a transparency-logging option that lets users review an Apple Intelligence & PCC Report in device settings. This is an Apple-specific control, not a general feature available across phone platforms.
Eligibility changes
The Apple support page cited for this topic lists Apple Intelligence on iPhone 15 Pro, iPhone 15 Pro Max and iPhone 16 models or later. Eligibility can also depend on software, language and region, so check Apple’s current support page for the exact model and configuration before buying a phone or promising that a feature will be available.
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Android Gemini Nano: a documented local path, not a blanket Android rule
Google’s Android developer documentation says Gemini Nano runs in Android’s AICore system service, using device hardware for low-latency, on-device inference. Google’s ML Kit GenAI APIs are built on AICore to support this execution model, and Google says Gemini Nano can work without a network connection or sending data to the cloud.
That documentation establishes a local path for Gemini Nano and related APIs. It does not mean every Gemini-branded feature, every Android assistant request or every Android phone processes prompts locally. Confirm that the named phone, operating-system version and feature support Gemini Nano before drawing a privacy or offline-use conclusion.
Does local processing mean the entire feature is private?
No. It means the inference for that particular task occurs on the phone. The surrounding feature may still contact servers for an account, synchronization, telemetry, document retrieval or another operation. Ask two separate questions: where did the model run, and what other data flows does the feature use?
Cloud processing also requires a precise scope. For Apple Intelligence, Apple says PCC processes only data relevant to the request and removes it afterward, with user data required not to remain accessible after the response. That is a description of Apple’s design and stated requirements, not proof that all cloud AI systems follow the same policy.
Quick Recap
What changes between local, cloud and hybrid routes?
| Question | On-device route | Cloud route | Hybrid route |
|---|---|---|---|
| Where does inference occur? | On the phone for the named feature and request. | In the provider’s remote compute environment. | The product selects local or remote processing per request or feature. |
| Can it work without a network? | Possibly, when the supported feature is designed for offline use; Google documents this for Gemini Nano. | Normally requires network access to reach the service. | Local-supported requests may work offline; cloud-routed requests generally do not. |
| What is transmitted? | The model prompt need not leave the phone for inference, but other app operations are not established by that fact. | The service receives the data needed for remote processing; Apple documents a PCC request containing the prompt, desired model and inference parameters. | Transmission depends on which path the feature chooses. |
| Privacy controls or reporting | Feature-specific; no universal phone-wide rule is established here. | Provider-specific. Apple documents PCC privacy requirements and an Apple Intelligence & PCC Report. | Use the product’s documentation and any available logs; do not assume a universal control. |
| Supported models and regions | Limited by the phone, AICore or operating-system support and the feature. | Limited by the provider, account, software and service availability. | Both local and remote eligibility can affect the result. |
| Latency, battery, heat and quality | Trade-offs vary by task and device; no comparable current cross-platform measurements are established here. | Can use a different or more computationally capable model, with network delay. | Depends on the selected model. Apple’s developer documentation says lower reasoning effort reduces latency while deeper reasoning trades latency for more analysis. |
How to find out what your phone is doing
- Name the exact feature. “AI on Android” or “Apple Intelligence” is too broad. Identify the assistant, writing tool, image generator or API.
- Check the provider’s technical documentation. Look for terms such as on-device, AICore, Private Cloud Compute, server-based model, offline or network requirement.
- Verify eligibility. Check the current phone model, operating-system version, language, region and account requirements.
- Test the documented offline behavior. If the provider says the feature supports offline use, disconnect the network and try a supported task. An offline failure does not by itself prove that every online use is cloud-based; the feature may have changed routes or require another service.
- Review privacy settings and reports. Use any feature-specific transparency log or report, such as Apple’s Apple Intelligence & PCC Report, and read what the provider says about retention and deletion.
- Separate inference from companion services. Even a local model may coexist with synchronization, retrieval, telemetry or account traffic.
What you can safely conclude
- On-device means the model inference for the named task runs on the phone.
- Cloud means the request is processed remotely.
- Hybrid means the product can choose either route, often based on complexity or available capacity.
- A feature’s route is not a property of the phone brand alone.
- Claims such as “your prompts never leave your phone” are too broad unless they are limited to a specific feature, model and documented condition.
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