设备端人工智能与手机上的云人工智能

On-device AI runs model computation on the phone's processor, GPU, or neural processing unit. Its advantages can include working offline, lower network delay, and keeping the request on the device for that operation. Those benefits depend on how the feature is built: apps may still sync results, use analytics, or contact a server for other functions. A local model also has limits in memory, compute, battery, and update cadence. Cloud AI sends some input to a remote service for processing. Larger models and centralized updates can support more complex tasks without requiring every phone to contain large model files. The tradeoffs include network availability, round-trip latency, service costs, provider data handling, and dependence on current terms and retention practices. A cloud request may be encrypted in transit, but encryption alone does not answer who can process or retain the content. Many phones use a hybrid approach. A device may first attempt a local model, then route a request to a cloud service when a task needs more capacity, or offer a setting that selects a mode. The interface may not expose every routing decision. Read the feature's documentation and privacy notice, look for network indicators or controls, and test offline behavior if it matters. Do not infer that a whole assistant is local just because one model runs on-device. For a fair comparison, test the same task on the same device and network. Measure response time, battery use, output quality, and what happens when the connection drops. Check whether the phone's model can be updated, whether processing changes across languages, and whether a feature sends context such as location or selected text. Use less sensitive inputs when the data route is unclear. Device makers describe hardware and model capabilities, but actual feature availability depends on the phone model, operating system, region, language, and app version.