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You can use a local language model for writing by installing a runner such as LM Studio or Ollama, downloading a compatible model, and running it on your computer. Local inference can keep prompts and documents on-device, but privacy depends on using the local model rather than optional cloud features. Check your computer’s memory and the runner’s requirements before choosing a model.
What “local” means for privacy
With local inference, the downloaded model generates its response on your computer. LM Studio says its offline mode processes documents locally and that uploaded material does not leave the application; its documentation also distinguishes local models from cloud models and web search. It says those cloud features process data transiently. Ollama says it does not see prompts or data when run locally. These are statements from the vendors about their products, not a guarantee covering every configuration or optional feature. See LM Studio’s offline-operation documentation, its cloud documentation, and Ollama’s privacy information.
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Before using sensitive drafts, confirm that the selected workflow is using a downloaded local model. Avoid cloud models and web search if you do not want the request handled by an online feature. If you need the computer fully offline, download the runner and model files first, then disconnect and check that your intended workflow still works.
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Model size and context length both affect memory use. A runner’s system recommendation is a starting point, not a promise that every model will run quickly or produce useful writing on that hardware.
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| Platform | LM Studio recommendation | Qualification |
|---|---|---|
| Apple Silicon Mac | 16 GB or more of RAM | LM Studio says Macs with 8 GB may work with smaller models and modest context sizes. |
| Windows | At least 16 GB of RAM and 4 GB of dedicated VRAM | These are LM Studio’s recommendations, not requirements for every model or runner. |
LM Studio’s current system-requirements page also lists platform notes for Windows x64 and ARM and Linux x64 and ARM64. Check the current requirements for your specific operating system and hardware. The figures above are vendor recommendations, not independent speed or quality benchmarks.
Choose a local runner
There is no established speed, privacy, or writing-quality winner among the options below. Choose based on the interface and workflow you want, then verify which model formats and operating systems the current release supports.
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| Runner | Workflow | Relevant documentation |
|---|---|---|
| LM Studio | Desktop application with a guided interface and documented offline operation. | Offline operation |
| Ollama | Another option for running models locally. | Privacy information |
| llama.cpp | A more direct runtime and model-file workflow; its documentation identifies GGUF as the format it runs. | llama.cpp documentation |
Set it up and try a low-risk writing task
- Check your system. Note your operating system, installed RAM, and available GPU memory, then compare them with the chosen runner’s current requirements.
- Pick a workflow. Choose a desktop interface such as LM Studio, Ollama, or the more direct llama.cpp route according to the setup you are comfortable managing.
- Install the runner and obtain a compatible model. Use the runner’s official source, and confirm that the model’s format is supported by that runtime. For llama.cpp, consult its documentation for GGUF support.
- Start with a modest model and context setting. Keep the choice within your available memory. LM Studio specifically advises smaller models and modest context sizes for Macs with 8 GB of RAM.
- Test with non-sensitive writing. Ask for an outline, alternate phrasings, or feedback on a paragraph before submitting private material. Review factual claims yourself and edit the output to preserve your voice.
- Confirm the privacy mode before sensitive use. Make sure the request goes to the downloaded local model, not an optional cloud model or web-search feature.
What to expect from a local writing assistant
A local model can help brainstorm, rephrase, outline, or critique text without relying on a hosted assistant for that inference. Its usefulness depends on the model, its size and quantization, the context length, the task, and the computer running it. The available official materials do not establish a universal best writing model or comparable writing-quality and speed results across hardware. Treat the first model as a trial, and judge it on your own tasks rather than assuming it will match a hosted assistant.
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