Chrome’s built-in AI can process supported tasks locally after its model is downloaded, while cloud AI sends requests to a service for processing. Local inference can work offline and Chrome says use of its built-in model sends no data to Google or third parties; cloud services can offer different capabilities and access to powerful infrastructure, but require a network connection and involve sending input to a server. Neither approach is universally faster or better. The right choice depends on what you need to do, whether your device qualifies, and which data flows you accept.
What “Chrome on-device AI” means
This comparison is about Chrome’s developer-facing built-in AI APIs, not every AI feature a person might encounter in Chrome. Chrome manages the local models used by these APIs. Its Prompt API uses Gemini Nano, and Chrome’s documentation also lists APIs for summarizing, writing, rewriting, proofreading, translation, and language detection. The set of APIs and their availability can change; check the current status of the specific API you intend to use in the built-in AI documentation.
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Local inference happens on the device after the required model is downloaded. A cloud-hosted AI service instead processes a request on a server. Chrome’s extension guidance discusses both approaches, but this does not mean every extension, website, or consumer-facing Chrome feature uses the same data path.
Chrome on-device AI vs. cloud AI at a glance
| Factor | Chrome on-device AI | Cloud-hosted AI |
|---|---|---|
| Where processing happens | On the user’s device, using a Chrome-managed model after download. | On the service provider’s server; the request must be sent over a network. |
| Privacy and data flow | Chrome says, “No data is sent to Google or any third party when using the model.” This statement is specifically about use of the built-in model. | Input sent to a server is shared with that service. The provider’s current terms and the application’s privacy disclosures determine how it is handled. |
| Network and offline use | An unmetered connection is needed for the initial model download. Afterward, use of the downloaded model can work without a network connection. | Requires connectivity to make a server request; the cited Chrome guidance does not quantify service uptime or latency. |
| Device support | Requires a supported operating system, enough free storage, and specified CPU or GPU capability. The documented foundation-model API requirements exclude mobile Chrome and non-Chromebook Plus ChromeOS devices. | Chrome describes cloud AI as potentially supporting a wider range of platforms and devices, but actual support depends on the service. |
| Speed | Chrome says inference is typically faster on capable GPUs than on CPUs. No direct comparison with cloud response times is provided. | Cloud platforms can provide powerful infrastructure, but the cited material gives no local-versus-cloud benchmark. |
| Capabilities and updates | Uses browser-managed models for specific API tasks; API availability and maturity differ by feature. | Chrome describes cloud platforms as offering scale, access to current hardware and software, and regular updates. Specific capabilities depend on the provider and service. |
| Typical fit | Self-contained tasks where local processing, offline availability, and a supported device matter. | Tasks that need capabilities not suited to the local model, provided the server data flow is acceptable. |
The trade-offs in this table reflect Chrome’s product guidance, not an independent performance or privacy audit. For cloud AI, review the chosen provider’s current terms and the application’s own privacy policy.
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Privacy: what stays local, and what does not
Chrome’s built-in AI documentation states: “No data is sent to Google or any third party when using the model.” Read that as a claim about using the built-in model—not as a blanket guarantee about Chrome, extensions, websites, telemetry, cloud fallback, or all other data flows.
If an extension sends user input to a cloud service, that information leaves the device. Chrome’s extension AI guidance says an extension’s privacy policy should explain what is shared. For sensitive material, local processing may reduce the need to transmit content for inference, but a reader should still assess the full application and its other data flows.
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Speed: is on-device AI faster than cloud AI?
There is no supported universal winner. Chrome says capable GPUs typically run inference faster than CPUs, and its October 1, 2025 announcement of CPU inference support for Gemini Nano in Chrome 140 says response times can differ between CPU and GPU inference while the model remains consistent. The cited Chrome sources do not give numerical comparisons between local inference and cloud services.
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Actual response time depends on the device, model, task, and network conditions. A cloud service may use powerful server hardware, while local processing avoids waiting for a server round trip; those observations do not establish which will be faster for a particular workload. If speed matters, compare the same task on the device and service you expect to use.
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Requirements for Chrome’s built-in foundation-model APIs
Chrome’s published requirements apply to its documented foundation-model APIs and can change with browser versions and API status. The current documented list includes:
- Operating system: Windows 10 or 11, macOS 13 or later, Linux, or ChromeOS on Chromebook Plus from Platform 16389.0.0.
- Free storage: At least 22 GB on the volume containing the Chrome profile. This is a minimum free-space requirement, not a stated model download size.
- Processor: Either a GPU with strictly more than 4 GB of VRAM, or a CPU with at least 16 GB of RAM and four cores.
- Prompt API audio input: Requires a GPU, even where CPU inference is otherwise supported.
These requirements come from Google’s built-in AI setup documentation; they should not be generalized to every Chrome AI feature. That documentation says the models are not available on mobile, and its foundation-model API list excludes Chrome for Android, iOS, and ChromeOS devices that are not Chromebook Plus. It also describes Translator and Language Detector as desktop-only.
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Downloading the model and checking availability
The first use requires downloading the model. Chrome says an unmetered connection is needed for the initial download; subsequent use of the downloaded model does not require a network connection. The model’s exact size can vary with browser updates, so Chrome directs users to chrome://on-device-internals to inspect it.
Chrome documents four availability states: unavailable, downloadable, downloading, and available. A user interaction may be required to start a session while a model still needs downloading. If free storage on the relevant volume falls below 10 GB after download, Chrome says it removes the model and downloads it again once requirements are met.
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For the Prompt API specifically, Gemini Nano is downloaded separately the first time an origin uses the API. See the current Prompt API documentation for its status and requirements. API availability is not uniform: for example, Chrome’s Summarizer API documentation says it is available from Chrome 138 stable, while the Writer API documentation describes an origin trial running in Chrome 137 to 148. These examples are feature-specific, not a promise that every listed API is generally available in stable Chrome.
Which option should you choose?
Choose on-device processing when
- The task fits a supported built-in API and your device meets its requirements.
- You want to use the model offline after completing its initial download.
- Keeping the inference input on the device is important, and the application’s other data flows are also acceptable.
Choose cloud AI when
- You need a model or capability not available through the local API for your device or task.
- You prefer the service’s infrastructure or update model, and its current data-handling terms meet your needs.
- You can rely on a network connection and are comfortable sending the request to the provider.
Consider a hybrid approach for complex tasks
Chrome’s extension guidance recommends considering hybrid approaches for complex work or unsupported devices: use local processing where it fits and a cloud service where additional capability is needed. That choice also creates a decision point about when information leaves the device, so the application should make that flow clear.
Bottom line: decide by task, data flow, and device
Chrome on-device AI is a practical fit for supported tasks when offline use or local inference matters and the device meets the published requirements. Cloud AI may suit work that needs a different or more capable model, but it requires a server request and a deliberate choice about sharing input. Chrome’s sources establish neither a universal speed winner nor a blanket privacy guarantee across all Chrome features; check the relevant API and provider, then measure performance on the actual task if it is decisive.
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