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To run an LLM fully offline, prepare everything while the computer is connected, then make the local runtime, model files and dependencies work before you cut network access. Install a local runtime such as LM Studio for a graphical workflow or llama.cpp for command-line control, download compatible model weights, load the model, and test a prompt. Then disable telemetry, update checks, web search, plugins, remote connectors and cloud synchronization, block outbound traffic with the operating-system firewall or disconnect the computer, and test again.
Offline inference is an operating condition rather than a single download. A model running from local storage does not automatically stop optional features from contacting the internet, so privacy depends on auditing those features and checking the machine again after software updates.
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What “fully offline” actually means
A local model performs inference on your computer instead of sending the prompt to a hosted service. “Fully offline” adds a stricter requirement: after preparation, the machine must not need an internet connection for chat, model loading, authentication, telemetry, updates, search, extensions or connected tools.
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What remains available without a network
- Prompting the model in a local desktop interface.
- Running a local API for applications on the same machine or trusted local network address.
- Loading model files already stored on the computer.
- Changing local generation settings, conversation history and system prompts, subject to the runtime’s own features.
What stops working
- Downloading a new model or runtime update.
- Cloud-based search, remote retrieval and hosted plugins.
- Connectors that call an external service.
- Telemetry or crash reporting that requires a remote endpoint.
- Cloud synchronization of chats, settings or files.
Plan for those trade-offs before disconnecting. An offline installation is most reliable when it includes the installers, model weights, dependencies, licenses and checksums needed to reproduce the setup later.
Choose the local runtime that fits your workflow
Two practical paths cover most personal computers. LM Studio emphasizes a desktop interface and a low-friction setup. llama.cpp exposes more of the inference stack and is better suited to command-line automation, custom acceleration and a local server.
| Decision point | LM Studio | llama.cpp |
|---|---|---|
| Interface | Graphical model browser, chat and settings | Command-line tools and optional local server |
| Best for | Users who want a guided desktop workflow | Users who want scripts, custom builds or detailed control |
| Hardware paths | Uses the platforms supported by its desktop application | Build options include CPU, Vulkan, CUDA, Metal and SYCL-related accelerators |
| Local API | Provides local chat and can expose a local endpoint where supported | Includes an OpenAI-compatible local server option |
| Maintenance | Application updates and model management are integrated into the interface | You manage binaries, builds, model files and launch options |
| Offline preparation | Download the application and model files before disconnecting | Build or obtain the correct executable and model files before disconnecting |
When LM Studio is the sensible choice
Use LM Studio when you want to select a model, adjust settings and chat without learning a build system. Its documentation describes local use of models such as Qwen, Mistral, Gemma and gpt-oss. Once the model files are present, its local chat workflow can operate without an internet connection.
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Choose llama.cpp when you need repeatable scripts, a small headless installation, custom accelerator support or a local API that another application can call. Its official build documentation covers CPU and several accelerator routes, so the appropriate build depends on your operating system and hardware.
Estimate hardware needs without relying on a misleading minimum
There is no universal RAM or VRAM minimum for a local LLM. The requirement changes with model size, quantization, context length, operating system, runtime and backend. A small quantized model may run on a computer that cannot load a larger model, while the same model may run faster or support a longer context when more memory is available.
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The variables that consume memory
- Model size: More parameters generally mean a larger weight file and greater memory demand.
- Quantization: Lower-bit weights reduce storage and working memory, usually with a quality or flexibility trade-off.
- Context length: A longer conversation window requires additional working memory as tokens accumulate.
- Backend: CPU-only execution, GPU acceleration and split CPU/GPU execution place memory in different locations.
- Operating-system overhead: The runtime shares RAM or unified memory with the rest of the system.
- Storage speed: An SSD, especially an NVMe SSD, shortens model-loading time but does not replace the memory required during inference.
How to choose a model for your computer
- Check available system RAM and, if applicable, dedicated GPU memory or unified memory.
- Start with a model whose downloaded file and runtime requirements leave room for the operating system.
- Select a quantization supported by the runtime and test the context length you actually need.
- Increase model size only after a smaller model loads reliably and produces acceptable speed.
- If the model barely fits, reduce context length or use a backend that can share work between CPU and GPU.
LM Studio’s system-requirements documentation lists supported CPU and GPU families for Mac, Windows and Linux. It specifically cautions: “You may still be able to use LM Studio on 8GB Macs, but stick to smaller models and modest context sizes.” That is a platform-specific warning, not a universal minimum for every local runtime or model.
Prepare everything while you are online
Do the preparation in a deliberate order. The goal is to reach a working local chat before any network restriction is applied.
- Choose the runtime. Install LM Studio for a graphical workflow, or obtain/build llama.cpp for command-line and server use.
- Select the model. Confirm that its format is supported by the chosen runtime, read the license, and record the exact model name and quantization.
- Download the model weights. Store them in a permanent local directory rather than a temporary downloads folder.
- Collect dependencies. For llama.cpp, this can include the correct executable or build artifacts and any accelerator-specific components. For a desktop runtime, keep the installer available for reinstallation.
- Record integrity information. Save any checksum supplied with the model or installer and note the file size, download date and source.
- Test local inference. Load the model, send a prompt and wait for a complete response. Test a second prompt after restarting the runtime so you know the setup does not depend on a cached session.
- Test the API if you need one. Confirm that the application points to a local address such as localhost, not a hosted endpoint.
- Export or copy what you need. Keep installers, model files, licenses, configuration notes and any required runtime components together on local storage.
Run an LLM offline with LM Studio
LM Studio is the easier route when you prefer a desktop interface. Names and locations can change between application releases, but the sequence is stable: install while online, download a model, verify local chat, then remove network dependencies.
Install and obtain a model
- Install the version appropriate for your operating system while the computer is online.
- Use the model area to find a compatible model, or import a model file you have already downloaded.
- Read the model’s license and usage conditions before using it for work or redistribution.
- Choose a quantization that fits available memory and leave headroom for the operating system and context.
- Wait for the model to finish downloading, then confirm that the file is stored locally.
Verify local chat
- Open the chat view and select the downloaded model.
- Send a short prompt that does not require web access, such as a request to explain a paragraph you provide.
- Close and reopen the application, select the same model again and repeat the prompt.
- Open the application’s local-server or developer controls only if you need another program to connect.
- If you enable a server, bind it to a local address unless you have a specific reason to expose it elsewhere.
Remove optional network paths
Before disconnecting, review every setting that can contact a remote service. Turn off telemetry, automatic update checks, web search, plugins, remote tool connectors and cloud synchronization. A model can remain local while one of these add-ons still sends data or attempts a network request.
Run an LLM offline with llama.cpp
llama.cpp is appropriate when you want a small, scriptable runtime or need to select an accelerator build yourself. The exact build command depends on the operating system and accelerator, so use the build option that matches the hardware you intend to run after disconnection.
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Install or build the runtime
- While online, obtain the llama.cpp source or a suitable prebuilt package.
- Select the build path for your machine: CPU, Vulkan, CUDA, Metal or a SYCL-related option where supported.
- Build or install the command-line executable and the local server executable if you need an API.
- Run the executable’s help output and save it with your setup notes. This records the options available in the exact build you tested.
Load a model from local storage
Place the model file in a directory that will remain available after the network is removed. Launch the command-line client with the model path and a short prompt, for example:
llama-cli -m /models/model-file -p 'Explain this sentence in plain language.'Use the model format expected by your build. If the executable reports an unsupported format, do not keep retrying with random flags; obtain a compatible file or rebuild the runtime with the required support.
Expose a local API when required
For an application that expects an OpenAI-compatible interface, start the local server executable with the model file and a loopback host. A typical pattern is:
llama-server -m /models/model-file --host 127.0.0.1 --port 8080Confirm that the client is configured for the same local address and port. Test a request while online, then repeat it after the network is disabled. Keep the server bound to loopback unless you have deliberately configured authentication and a trusted local network.
Make the offline boundary enforceable
Software settings reduce accidental connections; a network boundary prevents a missed setting from undermining the result. Use one of these controls, or both for sensitive work:
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- Disconnect the computer: Unplug Ethernet and disable Wi-Fi and Bluetooth networking that could provide a route back to the internet.
- Block outbound traffic: Create an operating-system firewall rule that denies outbound connections for the runtime and any helper process. Apply the rule before the final offline test.
Windows
Open the Windows firewall controls from Settings → Privacy & security → Windows Security → Firewall & network protection. Use the advanced outbound-rule controls to block the runtime executables you installed. Check that the rule applies to every network profile you use.
macOS
Use System Settings → Network → Firewall to review the host firewall. For a stricter outbound policy, use a firewall tool that supports application-level outbound rules and add the runtime, server and helper processes separately.
Linux
Use the firewall framework already managed on the machine and create an outbound-deny rule for the runtime or its user account. Verify the rule with the firewall’s status and logging tools rather than assuming that an enabled firewall blocks outbound traffic.
Firewall interfaces vary by release. The important test is empirical: with the rule active, the local model must still load and answer, while a deliberate network request from the runtime fails.
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- Disconnect the network or enable the outbound firewall rules.
- Restart the computer or runtime so cached authentication and connections are not mistaken for offline operation.
- Load the model from its local path.
- Send a prompt using only text or files already stored on the machine.
- If you use a local API, send a request to the loopback address and confirm a response.
- Try a feature that should be unavailable, such as web search, and confirm that it is disabled rather than silently attempting a connection.
- Review firewall logs or network-monitoring output for unexpected outbound attempts.
A successful response proves local inference works; it does not prove that every optional component is network-free. Repeat this audit after installing updates, adding plugins or changing model-management settings.
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Common failures and how to recover
The model will not load
- Check that the model format matches the runtime.
- Confirm that the file is complete and compare its checksum if one was supplied.
- Try a smaller model or lower quantization demand so the operating system retains memory.
- Reduce context length and close other memory-heavy applications.
Generation is extremely slow
- Confirm that the intended accelerator backend was selected instead of CPU-only execution.
- Use the build that matches your GPU or integrated-graphics stack.
- Reduce model size or context length.
- Move model storage to a faster SSD if loading, rather than token generation, is the bottleneck.
The application keeps asking for internet access
- Disable update checks, telemetry, web search, plugins, remote connectors and synchronization.
- Check for a separate helper or server process and block it too.
- Restart after changing settings, then test with the network disconnected.
The local API cannot connect
- Confirm that the server process is running and listening on the address and port configured in the client.
- Use the loopback address when both programs run on the same computer.
- Check that the firewall allows local traffic while denying outbound internet access.
- Verify that the client is not still configured with a hosted endpoint or cloud API key.
The model worked online but fails offline
This usually means the setup depended on a missing download, authentication step, remote connector or cloud-synchronized file. Reconnect only long enough to identify and obtain the missing local dependency, then repeat the offline verification from a clean restart.
Privacy, licensing and reproducibility
Offline execution limits network exposure, but it does not make the computer automatically private. Prompts can remain in local chat history, application logs, shell history, backups or crash dumps. Set local retention and backup policies appropriate to the sensitivity of your data.
Model licenses can restrict commercial use, redistribution, modification or particular applications. Record the license alongside the weights and runtime. If you share the setup with another computer, transfer the license and checksum information as well as the model file.
For reproducibility, keep a small inventory containing the operating system, runtime release, build options, model name, model format, quantization, context setting and firewall state. This turns an offline installation from an improvised experiment into a setup you can restore without guessing.
Choosing between convenience and control
| If your priority is… | Prefer | Reason |
|---|---|---|
| Minimal command-line work | LM Studio | The graphical workflow handles model selection, loading and chat in one application. |
| Automation and repeatable scripts | llama.cpp | Command-line tools can be launched from scripts and scheduled jobs. |
| Custom accelerator selection | llama.cpp | You can choose among documented CPU and accelerator build paths. |
| Local application integration | Either, after testing the local endpoint | The deciding factor is whether the client supports the server interface and address you configure. |
| Small maintenance burden | LM Studio | Model and application management are presented through a desktop interface. |
Neither choice removes the need to manage model files, licenses, memory limits and network controls. The runtime changes how you operate the model; it does not change the hardware demands of the selected model or make optional network features safe by default.
A practical offline checklist
- Runtime installed and tested while online.
- Model weights downloaded in a supported format.
- Model name, quantization, license and checksum recorded.
- Local prompt completed after a restart.
- Local API tested, if required.
- Telemetry, update checks, web search, plugins, remote connectors and synchronization disabled.
- Outbound firewall rules applied or all network interfaces disconnected.
- Offline prompt completed successfully.
- Installers, dependencies and model files stored locally for recovery.
- Hardware and configuration inventory saved for future reproduction.
The Bottom Line
The dependable pattern is simple: download and validate the runtime and model while connected, prove local inference works, remove every optional network path, enforce the boundary with disconnection or outbound firewall rules, and test again from a clean offline state. Select model size and context according to the memory your computer actually has; there is no universal RAM or VRAM minimum.
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