Run Local LLMs for Desktop Privacy

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Start with the information you want to protect

If you only need to draft ordinary text, the simplest question is whether local inference is worth the model download, storage, and hardware demands for you. If you intend to paste confidential notes or use document chat, ask a stricter question: which app feature handles the material, and does that feature run locally? The answer can differ within one product.

LM Studio's offline operation guide says that downloaded models, document chat, and its local server can work without an internet connection. It also identifies online operations such as discovering and downloading models and runtimes. LM Studio says local chats and documents stay on the device. Those statements describe the provider's documented behavior; they are not an independent inspection of every version, extension, or operating system. Last verified: 2026-10-12.

Ollama's FAQ draws a similar boundary with an important exception. It says local execution does not send prompts and responses to Ollama, while cloud hosted models are processed by its service. The FAQ documents a local only setting that disables cloud models and web search. A reader who wants an offline setup should check whether cloud features are enabled before using it with private material. Last verified: 2026-10-12.

Pick the interface that fits your work

Choose LM Studio if a desktop interface, document chat, and a local server suit your work. Its documentation explicitly separates those local functions from catalog searches, downloads, and updates. Choose Ollama if you want a local model runner that you can use from a terminal or through its API, and read its local only configuration before assuming an offline workflow. These are fit judgments based on documentation, not benchmark rankings or hands on recommendations.

Neither option removes the need to obtain model files or to consider model licensing, available memory, storage, and the behavior of integrations you connect. We have not measured speed or compared model quality on Windows or macOS; model size, format, hardware, and settings change the result. Avoid a blanket RAM or GPU shopping list until you have chosen a specific model and checked its published requirements.

A privacy check before you paste a real document

1. Choose a model that is already downloaded and confirm you are using the app's local inference path. For Ollama, review its documented cloud setting. For LM Studio, distinguish local chat or document chat from its online catalog and downloads. 2. Start with a harmless sample document. Confirm that the output fits the task and review any plugins, custom nodes, browser integrations, or remote endpoints separately. 3. If your use requires no network activity, repeat the workflow while disconnected, after all model and runtime downloads are complete. A successful offline run is useful evidence for that specific setup; it does not certify the software or every future update. 4. Apply your organization's data policy before putting confidential information into any model, local or remote.

The useful distinction is local inference versus connected features, rather than a promise that a desktop app is private in every configuration. If your work concerns pictures rather than text, the local image generation guide covers a different set of model and workflow choices.

Optional background reading: Hands-On Large Language Models explains language-model concepts; the authors' companion repository lists its materials. It is not required for either app or the privacy checks above. Last verified: 2026-10-12.