You can use a local language model to brainstorm, revise, or critique writing by installing a local runner, downloading a compatible model, and running it on your own computer. For sensitive work, confirm that you are using the downloaded model rather than an optional cloud feature: “local” describes where inference runs, not every feature an app may offer.
What you need before you start
Check your operating system, system memory (RAM), and—on a Windows PC—available dedicated graphics memory (VRAM). Model size and context length also affect whether a model fits; a runner’s system recommendation is not a guarantee that every model will run well or produce useful writing.
As of LM Studio’s System Requirements page, the vendor recommends 16 GB or more of RAM for Apple Silicon Macs running macOS 14 or newer, and says Macs with 8 GB may work with smaller models and modest context sizes. For Windows, LM Studio recommends at least 16 GB of RAM and at least 4 GB of dedicated VRAM. These are LM Studio recommendations, not universal minimums or performance benchmarks. Its requirements page also lists platform notes for Windows x64 and ARM, and Linux x64 and ARM64: LM Studio system requirements.
Choose a local runner
| Option | Workflow | Model/runtime note |
|---|---|---|
| LM Studio | Desktop application with a guided interface; its documentation describes offline use. | Check its current platform requirements and use a model supported by the application. |
| Ollama | Another option for running models locally. | Its privacy statement applies to local operation; check whether optional features or a different mode are active. |
| llama.cpp | A more direct, lower-level runtime workflow. | Its official documentation identifies GGUF as the model format it runs. |
The available official materials describe these options but do not provide an independent, apples-to-apples comparison of writing quality, speed, or overall security. Choose based on whether you prefer a graphical interface or a more hands-on runtime, and on compatibility with your computer and the model you want to use. Sources: LM Studio offline operation, Ollama, and llama.cpp.
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Set up a local writing workflow
- Check your hardware. Note your operating system, RAM, and available VRAM, then compare them with the current requirements for your chosen runner. Treat recommendations as guidance, not a promise that a particular model or context length will fit.
- Choose the runner. Use LM Studio if you want a desktop interface, Ollama as another local-runner option, or llama.cpp if you are comfortable with a lower-level workflow and GGUF model files.
- Install the runner and obtain a compatible model. Download the software from its official source and confirm that the model’s format is supported by the runtime. If you expect to work offline, download both the runner and model before disconnecting.
- Start modestly. Select a model size and context length that suit your available memory. LM Studio specifically advises smaller models and modest context sizes for 8 GB Macs. No single model-size setting or token speed can be promised across devices.
- Try a low-sensitivity task first. Ask for outline ideas, alternate phrasings, or feedback on a paragraph. Check factual claims yourself and edit the result to preserve your voice; treat output as a suggestion, not an authority.
- Verify the privacy mode before using sensitive material. Confirm that the active workflow uses a downloaded local model, not a cloud model, web search, or another feature that sends information for processing.
- Test offline operation if that is a requirement. After downloading what you need, disconnect from the network and check that the selected workflow still works. This is a practical check for your configuration, not a guarantee that every feature is available offline.
What “private” and “local” mean in practice
When inference runs with a downloaded model on your computer, prompts and documents can remain on that device. LM Studio says in its Offline Operation documentation: “All document processing is done locally, and nothing you upload into LM Studio leaves the application.” Its documentation also distinguishes downloaded local models from cloud models and web search, which it says process data transiently. Ollama says it does not see prompts or data when it is run locally. These are statements from the vendors about their products and modes, not an independent audit of every configuration.
For a sensitive draft, check which model and features are active before pasting text. Avoid cloud models and web search if you require local processing, and consult the relevant vendor documentation for the workflow you are using. LM Studio’s description of its offline features is available at LM Studio offline operation; Ollama’s privacy information is at Ollama.
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Choosing a model for writing
There is no established universal best writing model in the cited official materials, and they do not offer a directly comparable writing evaluation. Results depend on the task, model, quantization, available memory, and context length. Try a model that fits your computer on a low-stakes passage, then judge whether its edits are useful, accurate, and consistent with your voice before using it for more demanding work.
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