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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteYes—local coding models can generate responses without an internet connection once the model, runtime, and editor integration are installed and configured. But “local” does not mean every coding-assistant feature works offline: some editors still depend on cloud services for inline suggestions, semantic search, or embeddings, and downloads, updates, and telemetry may also need attention.
What “offline” means for a coding assistant
Offline inference means your computer sends prompts to a model running locally rather than to a hosted model service. Ollama documents making local model requests without an API key, and VS Code says locally hosted models can be used without internet access. That does not, by itself, make the editor, extension, or every connected feature offline.
Prepare while connected: install the runtime and editor extension, download the model files, and configure the coding assistant to use the local provider. Once disconnected, inference can work as long as those components are available on the machine. Model downloads, extension updates, and online services will not be available until connectivity returns.
Keep local inference distinct from cloud-hosted models offered by the same provider. For example, Ollama’s local model use is separate from Ollama cloud models, which require an online service. See Ollama and its FAQ.
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Which coding features may still need the internet?
Feature availability depends on the editor and extension, not just where the model runs. In VS Code’s BYOK local-model route, chat and configured utility tasks can use a local model, while some capabilities remain tied to GitHub services. Microsoft says semantic search, inline suggestions, and embedding-dependent functions require a GitHub account and internet connectivity; its documentation also says a local model cannot currently be connected for inline suggestions. Check the current VS Code language-model documentation for the exact feature boundaries.
For a fully disconnected setup, account for non-model traffic too. Continue’s offline guide instructs users to download and install its VSIX, turn off “Allow Anonymous Telemetry,” select a local model in configuration, and restart VS Code. An extension that attempts telemetry requests is not fully air-gapped merely because inference runs locally. Follow the extension’s own instructions, such as Continue’s offline setup guide.
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How to prepare an offline setup
- While online, install the runtime and editor integration. Install the local model provider and the coding-assistant extension you plan to use; offline use cannot fetch missing software.
- Download the model in advance. Confirm that the model files are present locally and that the provider can load them before you disconnect.
- Configure the extension to use the local provider. Select the local model in the extension’s settings or configuration, rather than a hosted model.
- Review cloud-dependent features and telemetry. Disable telemetry if required for your disconnected or air-gapped workflow, and do not assume online-only editor features have local substitutes.
- Restart and test without connectivity. Try the actual tasks you need—such as chat or code edits—and verify that the extension is not relying on a remote endpoint.
What affects speed and usable context?
There is no universal minimum hardware requirement that guarantees a particular coding experience. The model, its quantization, context setting, runtime, available RAM and GPU memory, and the task all matter. A model may run on CPU, GPU, or a split between them; the provider’s diagnostics can help show where it is running.
Ollama’s FAQ documents a default context window of 4,096 tokens. The context window is the amount of prompt and conversation the model can consider at once, not a measure of code quality. Larger context windows require more memory. If there is not enough VRAM and the model uses system RAM, responses may be slower. Ollama explains how to inspect CPU/GPU placement and change context settings in its FAQ; actual throughput depends on the computer and configuration.
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What coding quality should you expect?
Local coding quality varies by model and task; “local” is not a quality level. A 2025 preprint by Matotek, Cassel, Amiruzzaman, and Ngo evaluated eight locally hosted code models with 6.7–9 billion parameters on 3,589 Kattis programming problems. In that benchmark, the best local models had approximately half the acceptance rate of the proprietary Gemini 1.5 and ChatGPT-4 comparison systems. The paper was accepted to CCSC 2025. That is a result for those models and competitive-programming problems, not a general measure of everyday programming or a prediction for every current local model. Read the study for its scope and results.
For your own work, test the model on representative tasks: explain a function, draft a small edit, diagnose a bug, or work with the amount of repository context you expect to provide. Also test the relevant editor features separately; a model that answers chat prompts locally may not provide local inline completions or repository-wide search in your chosen extension.
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