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Local AI Models Explained: Where Inference Happens and What It Means

A local AI model processes prompts on your device or local computer. “Local” identifies where inference happens, not whether the surrounding app is always offline or private.
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A local AI model runs its inference—the processing that turns an input into an output—on your device or another computer on your local network, rather than sending the inference request to a remote cloud service. The model may have been downloaded from the internet, and the app around it may still use online services, so “local” describes where inference happens, not a guarantee that the product is always offline or private in every respect.

What is a local AI model?

When you enter a prompt, inference is the step in which a model processes that input and generates a response. With local AI, that computation takes place on your phone, PC, or another nearby computer instead of on a provider’s remote servers. The model files can be bundled with an app or downloaded before use. Apple’s Core AI documentation describes both approaches for on-device models; Microsoft describes a similar download-and-run arrangement for Foundry Local.

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“Local” does not specify the model’s architecture, who trained it, or its license. It also does not necessarily mean the model is small, open source, or part of a fully offline app. Those are separate characteristics.

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Does local AI work offline?

It can, once the model and any required files are available on the device. For example, Microsoft says Foundry Local’s initial model download requires internet access, but inference using a cached model can continue offline. The software may still need a connection for setup, updates, or catalog metadata; inference and those supporting tasks are not the same thing. Microsoft’s Windows FAQ, last updated July 15, 2026, explains these distinctions in its Foundry Local guidance.

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Whether a particular app works entirely offline depends on more than its model. Check whether the app requires sign-in, online retrieval, cloud processing for certain features, or network access for other functions.

Does a local AI model send your data to the cloud?

The model’s inference request does not have to leave the device when it runs locally. Microsoft says Foundry Local inference inputs and outputs remain on the machine. That is a claim about that inference path, not a universal guarantee for every app or device configuration.

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An app can separately transmit diagnostics, refresh information, download content, or send a request to a cloud model when local processing is unavailable. Microsoft also says the owner of a local deployment remains responsible for security and updates. To assess privacy, review the app’s data practices and settings, and check whether it has cloud fallback or optional telemetry. The word “local” alone does not answer those questions.

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Local AI vs. cloud AI: what changes?

Factor Local inference Cloud inference
Where processing happens On the device or local computer. On remote provider infrastructure.
Network dependence Can work without a connection after setup if required model files are available. Typically depends on a network connection to reach the service.
Hardware and model capacity Limited by the available processor, memory, storage, and the model’s demands. Can draw on scalable infrastructure and larger models, depending on the service.
Data and security responsibility Can reduce prompt data sent to a provider, but app behavior and local security still matter. Prompt processing occurs on provider infrastructure, so the service’s data practices and policies matter.
Maintenance and scale Users or administrators may need to manage model files, updates, and device availability. The provider manages the serving infrastructure; access and scale depend on the service.

Local processing may reduce network dependence and avoid the delay of a round trip to a remote service, but actual speed and answer quality depend on the device, model, and task. Microsoft’s comparison, last updated September 21, 2026, discusses these tradeoffs—including latency, complexity, cost, maintenance, and scale—in its local-versus-cloud guidance.

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What computer do you need to run AI locally?

There is no single minimum specification that applies to all local AI models. The needed resources depend on the model and the workload. A device may use its CPU, GPU, or NPU, alongside available memory and storage; a more demanding model or task may require more capable hardware. Confirm the requirements of the particular model and the software that runs it before choosing a device. A device marketed with built-in AI features is not automatically compatible with every local model.

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When does a hybrid approach make sense?

A hybrid system can try local inference first, then use a cloud model when the local model is unavailable or cannot handle the task. This can combine local processing for supported work with access to greater cloud capacity for other requests. The tradeoff is that a fallback may send data off the device. A well-designed system should make that possibility and the relevant user consent or policy choices clear. Microsoft includes local-first processing with cloud fallback among the available design approaches in its comparison of local and cloud models.

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