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You can run a locally stored language model without sending prompts or code to a cloud service: download the model weights and install a compatible runtime first, then disconnect the computer and test local inference. For stronger protection, disable cloud features, keep any local API bound to loopback, and leave file, shell, and remote-service integrations off unless you specifically need them. Offline inference reduces exposure, but it does not by itself prove that every part of an app or computer is private.
What “offline” protects—and what it does not
In local inference, the model runs on your computer using model files stored there. LM Studio says that once a model is on the machine, chatting with it does not send entered content away; its document-chat workflow also stays on the machine. Those are vendor statements about the product, not an independent audit of every build, extension, or integration. See LM Studio’s offline-operation documentation.
Offline inference is different from setup and maintenance. Finding or downloading models, downloading runtimes, and checking for app updates may require network access. A disconnected workflow therefore means preparing those files beforehand and then preventing unwanted network access during use. Local execution also does not protect against malware, compromised dependencies, accessible backups, chat histories or logs, or another person with access to the computer.
Prepare the model and runtime before disconnecting
- Choose a model and check its terms. Confirm that the model is compatible with your operating system and runtime, and read the model’s license and usage terms. “Open-source” is not a guarantee that every model has the same permissions. Verify the model’s provenance and file integrity as well.
- Check the computer against the model’s needs. Review the model’s current documentation for memory, storage, and accelerator requirements, and confirm that the runtime supports your operating system and hardware. There is no universal RAM or performance figure that applies to every model and workload.
- Install the runtime and obtain the model files while online. LM Studio supports local inference on macOS, Windows, and Linux; it documents llama.cpp-based inference and, on Apple Silicon, MLX support. Its model discovery, model downloads, runtime downloads, and app update checks make network requests. You can also obtain model files outside the app and sideload them, as described in LM Studio’s sideloading guide.
- Optionally stage files on removable storage. An external SSD can help carry or store model files for a computer that will be disconnected. It is optional; LM Studio’s sideloading support does not require an external drive or specify a required capacity or speed.
Test local inference with connectivity disabled
After installation and model setup, disconnect the machine from the internet—for example, by turning off Wi-Fi and unplugging Ethernet—then start the runtime and send a non-sensitive test prompt. Confirm that the selected model loads and produces a response while connectivity remains off. If the task involves documents, test the document workflow in the same disconnected state. LM Studio says its document-chat/RAG processing stays on the machine, but that does not establish that unrelated plugins or integrations are local.
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A successful test demonstrates that this configuration can perform the tested inference without an internet connection. It does not establish that the application never makes network requests when connectivity returns, or that an altered build or add-on has the same behavior.
Keep local services and cloud features under control
Preserve loopback-only API binding
A local API is still a service that other processes on the same computer can reach. Ollama documents 127.0.0.1:11434 as its default address; the llama.cpp server example defaults to 127.0.0.1:8080. Loopback binding limits access to the local machine. Avoid changing the bind address unless another device genuinely needs access.
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If you deliberately enable LAN or public access, treat it as a different threat model: restrict which devices can connect and configure the relevant authentication, origin restrictions, firewall rules, and any proxy or tunnel controls. Ollama documents host-binding and proxy/tunnel options; llama.cpp advises access controls for public deployment and origin restrictions for local-network use. See Ollama’s FAQ and llama.cpp’s server documentation.
Disable Ollama cloud features if you want local-only use
Ollama’s FAQ documents two ways to disable cloud features: set the OLLAMA_NO_CLOUD=1 environment variable, or set "disable_ollama_cloud": true in ~/.ollama/server.json, then restart Ollama. Disabling cloud features also removes access to Ollama cloud models and web search. This setting is useful, but for stronger assurance, also block network access outside the application and inspect traffic in the deployment you actually use.
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Do not grant tools more access than the task needs
Offline does not mean harmless. Tools can expand what a local model-enabled application is able to do. llama.cpp’s optional tools can read or write files and execute shell commands, while MCP server processes run with the privileges of the server process. Keep these capabilities disabled unless necessary, and configure only tools you trust. A model that cannot send data over the internet may still access files or perform actions locally if you grant those permissions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Understand the privacy boundary
Ollama’s privacy policy, last updated March 2026, says it does not collect, store, transmit, or access prompts and responses processed locally. The policy distinguishes cloud-hosted models, for which prompts and responses are processed transiently, and says limited device and usage metadata may be collected, excluding prompt and response content. These statements apply to Ollama’s described local and cloud services; they are not a general guarantee for other runtimes, third-party tools, or modified installations. Read Ollama’s privacy policy.
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For any runtime, the practical privacy boundary includes more than the model: app features, add-ons, local APIs, system permissions, logs, backups, and the computer’s security all matter. If sensitive code or prompts are involved, use a dedicated, updated machine where possible, limit who can sign in, protect local storage, and enforce network restrictions at the operating-system or firewall level rather than relying only on an app setting.
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