On-device inference, clearly scoped
Run an AI model on the device that holds the task.
Local inference changes where a prompt is processed. It is one useful privacy boundary, not proof that an entire app is offline, network-free, or publicly available.
- AI inference runs on the endpoint when the local model path is used
- 0 local inference does not require sending its input to a remote inference server
- — release, purchase, and network details must be checked separately
Local inference is not the same as zero network traffic
On-device AI means the model performs inference on the endpoint device. In a cloud inference design, the prompt or other input is sent to remote infrastructure so a model there can produce the result. A local path can avoid that particular transfer: the input used for that inference does not need to travel to a model server. Google AI Edge documents Android routes for running Gemma locally, and LiteRT-LM provides an Android/JVM engine API. Those are descriptions of technology and integration paths, not an independent audit of Cove's full network behavior.
An app can still use the network for work that is separate from inference. Examples can include downloading a model before first use, checking a purchase, sending a user-requested email signup, or optional diagnostics. The exact services, settings, and data involved depend on the product and its implementation. A locally executed model does not prove that every other request is absent, nor that all data in every Cove app stays on the phone. Read the current privacy notice for the site's disclosed practices and check product-specific disclosures before making a decision.
Network inspection is evidence with boundaries, too. Android Studio's Network Inspector can show supported traffic, including supported OkHttp and HttpsURLConnection requests, in an inspected process. It does not promise visibility into every networking library or every execution path. A blank view should not be presented as proof that no traffic exists. Our wording follows the same rule: distinguish the inference path from the whole app, and distinguish an architecture description from a verified product audit.
Latency depends on the device, model, and task
Local inference removes the network round trip to a remote model server from that part of a workflow. It does not guarantee that the answer will be fast. The result still depends on the exact model artifact, runtime, device hardware, memory pressure, input length, and task. CPU, GPU, or NPU support in a framework is an available execution path; it is not proof that a particular phone uses that accelerator for a particular model.
We do not have validated raw benchmark records for the previously named phone and response-time claims. So this page does not promise a sub-second translation, quote cloud-versus-device timings, or call a named device supported or fast. Google AI Edge's documentation explains how a developer can configure a LiteRT-LM engine and backend. It does not certify Cove's exact model/device combination. A useful comparison requires a reproducible test of the same task, model, app build, settings, and device, with the raw observations kept alongside the conclusion. Until that evidence is published, latency is a measurement question, not a marketing statistic.
What can still work without a connection
After a compatible model has been obtained and the app has initialized its local inference path, that inference step does not need a remote model server. That is why on-device processing can be useful in a tunnel, on a plane, or where connectivity is unreliable. These examples describe the architectural benefit; they are not a statement that every Cove feature is currently usable in airplane mode.
First use may require a model download, and other app functions can have separate network dependencies. A practical check is to distinguish setup from use: obtain the model, read the product's current instructions, then test the specific feature with connectivity disabled. Note which step succeeds and which does not. A result for one task or release should not be generalized to other products, operating-system versions, or model builds. We publish no blanket promise that the whole Cove family works offline today.
The costs and limits are part of the design
A local model has to be delivered to the device and use device storage and working memory. The initial download can be substantial; the actual size depends on the model artifact and release, so consult the relevant product screen instead of relying on a stale number. Inference also competes for the phone's compute, memory, battery, and thermal headroom. A framework's ability to use CPU, GPU, or NPU does not tell you the speed, energy use, or sustained behavior on your phone.
A smaller or locally packaged model may not match a remote service on every language, long document, or specialized task. We have no published Cove benchmark set that would support a general quality or speed ranking. Consider the needs of the specific task, the languages you use, the available model, and whether an occasional connection is acceptable. If the work is high stakes, verify the result with an appropriate source or professional. Local processing can reduce a particular data transfer; it cannot guarantee correctness, confidentiality of every surrounding app function, or fitness for a particular decision.
Product plans and verified availability are different things
Cove is a family of products, but a shared design goal does not mean every app is complete or publicly downloadable. As checked on 27 September 2026, the website's download cards were configured for release notifications and no verified public APK URL or digest was published. Cove Travel and Cove Photo have release-ready source and publishing steps documented, but that does not itself establish a public release. Cove Voice remains in progress, and Cove Health still requires real-device safety validation.
For that reason, this page describes the local-inference architecture and the current evidence boundary; it does not extend a privacy guarantee to every byte in every product or present planned apps as available. Availability, model compatibility, network behavior, pricing, and performance should be confirmed from the live product disclosure and a verifiable release artifact. We will update these statements when that evidence changes. Until then, read “designed for local inference” as a description of an execution path, not a claim that a complete, audited, offline product is ready for download.
Check the current release information
Download cards currently offer release notifications. Review the privacy notice and product-specific details before signing up or installing an app.