Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →You can use a local AI model to discuss and refactor code in VS Code without sending prompts to a hosted model: connect VS Code chat to a model running on your computer through Ollama. That does not make every VS Code AI feature available offline, nor does it verify every extension or process on your device. The privacy benefit depends on using the local model path, not a cloud-hosted one.
What “offline refactoring” does—and does not—mean
In this setup, VS Code sends your chat request to a model served locally by Ollama. The model can help explain code or propose a refactor without sending that prompt and response to a hosted model. VS Code says local-model chat can work without an internet connection and without a Copilot plan. See Microsoft’s VS Code documentation on AI language models.
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Offline does not mean feature parity. VS Code documents that semantic search, inline suggestions, and features that depend on embeddings require GitHub service connectivity through its BYOK offline path. A local model also does not establish that the rest of the editor, installed extensions, or operating system has no network activity.
Ollama’s Privacy Policy, last updated March 2026, says it cannot access prompts and responses processed locally. It distinguishes these from cloud-hosted model requests, which are processed transiently to provide the service, and says it collects limited device and usage metadata that excludes prompt and response content. Those statements apply to Ollama’s described processing; they are not an independent security audit of VS Code, extensions, or your computer. Read the Ollama Privacy Policy for its full terms.
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Local model or cloud model?
| Factor | Local model through Ollama | Cloud-hosted model |
|---|---|---|
| Connectivity | VS Code says local-model chat can work without internet once the software and model are available. | Requires network access to the model service. |
| Code and prompt handling | Ollama says it cannot access prompts and responses processed locally. | Ollama says cloud prompts and responses are processed transiently to provide the service. |
| Editor features | Chat is supported in the documented offline workflow; semantic search, inline suggestions, and embedding-dependent features require GitHub service connectivity. | Connected services may provide features unavailable in the offline BYOK path. |
| Setup | Install Ollama, download a model, and configure VS Code chat to use it. The documented local setup does not require sign-in. | Use a hosted model provider and its required connection or account setup. |
| Model capability and local resources | Depends on the model and computer. The cited documentation does not establish a best refactoring model, minimum hardware, or quality comparison. | Depends on the provider and model. No general performance or privacy ranking is established here. |
Choose local chat when avoiding transmission of the code in your prompt is the priority and you can work within the local model’s capabilities. Choose a cloud workflow only after checking the provider’s handling terms and deciding that its connected features are worth the trade-off.
Set up Ollama chat in VS Code
Ollama’s VS Code integration documentation, retrieved October 7, 2026, lists VS Code 1.127 or newer, an installed and running Ollama instance, and at least one available model. It says the extension discovers Ollama at http://127.0.0.1:11434 by default. The built-in Ollama provider in VS Code is deprecated; the VS Code documentation points users to the official Ollama extension. Check the Ollama VS Code integration guide for current requirements and labels.
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- Install and start Ollama. Use Ollama’s official installation instructions for your operating system, then make sure the Ollama service is running.
- Install the official Ollama extension in VS Code. The documented workflow uses the extension rather than VS Code’s deprecated built-in Ollama provider.
- Download a model while connected, if necessary. In a terminal, run
ollama pull qwen3.6, the example command in Ollama’s guide. The model tag is an example, not a recommendation or a claim that it is best for refactoring. Local models do not require sign-in in the documented setup. - Open VS Code chat and choose the Ollama model. Select the locally available model through the chat or language-model interface. Confirm the selected provider is local, rather than a cloud model.
- Try a small request. Ask the model to explain a function or suggest a narrowly scoped change. If VS Code cannot find the model, confirm Ollama is running and that the extension can discover the local service at its default address.
Ollama’s integration guide recommends Ollama 0.17.6 or newer for cloud sign-in and richer model metadata; it says older versions may still work for local models. It also notes that earlier VS Code versions do not reliably cancel provider requests, which is one reason to meet the documented VS Code version requirement.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsKeep the refactor bounded and verify every change
A local model avoids sending the prompt to a hosted model when the local path is actually selected; it does not make generated code trustworthy by default. Treat suggestions as proposals and keep the review loop inside your normal development process:
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- Limit the context. Start with one function or a small, relevant set of files. Include only the code needed to explain the goal.
- Ask for a proposal and rationale. State what should remain unchanged, such as behavior or public interfaces, and ask the model to explain the trade-offs before applying a change.
- Inspect the diff. Review each changed line for unintended behavior, missing edge cases, unrelated edits, or accidental removal of comments and validation.
- Run the project’s checks. Use the repository’s existing tests, formatter, and static checks. Fix failures yourself or ask for a focused explanation, then inspect any new diff.
- Keep secrets out of prompts. A local inference path is not a reason to expose credentials, private keys, or data you do not need for the task.
The cited integration and privacy pages describe setup and data handling; they do not test refactoring accuracy. Your project checks and code review remain the evidence that a proposed change is acceptable.
When the available context seems smaller than expected
The context value shown in VS Code may not be allocated at that size by Ollama at runtime. For the workflow described in its integration guide, Ollama recommends setting the local context length to at least 64k in Ollama settings, reloading VS Code, and resending the prompt. This is configuration guidance for that integration—not a universal requirement for every model, a hardware-sizing promise, or a guarantee that a particular prompt will fit. See the integration guide’s context-length instructions.
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What still needs a connection?
Download and configure the model before going offline if it is not already available on the machine. Once disconnected, local chat can still work, but VS Code’s documented BYOK path does not provide service-dependent semantic search, inline suggestions, or embedding-dependent features without GitHub connectivity. If a task depends on those capabilities, plan to work online or change the task to one local chat can handle.
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