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Yes. A coding model can generate answers locally without an internet connection once its model files, runtime, and editor or agent configuration are installed. But offline inference is not the same as an entirely offline coding workflow: downloads, updates, telemetry, and some editor features may still depend on online services.
What “offline” means for a coding assistant
A local model runs inference on your computer rather than sending each prompt to a remote model service. For example, VS Code says locally hosted models can work without an internet connection, and Ollama documents running local models without an API key. You must have the model and compatible runtime installed before disconnecting; downloading them is a separate, normally online step. [VS Code] [Ollama]
The boundary matters: an editor can use a local model for chat while other parts of the editor still call cloud services. VS Code’s documentation says semantic search, inline suggestions, and embedding-dependent functions remain tied to GitHub services. Its current local-model route does not provide inline suggestions. Check the particular editor and extension rather than assuming that “local model” means every feature is local. [VS Code language-model documentation]
What to prepare before disconnecting
- Model and runtime: Install the model and a compatible local provider while you have connectivity. Ollama’s local requests do not require an API key; this should not be confused with Ollama cloud models, which are online services. [Ollama]
- Editor integration: Configure the editor or coding agent to use the local provider, then test the tasks you need while still connected.
- Extensions and dependencies: Install the editor extension and any required packages or tools in advance. Their downloads and updates may not be available offline.
- Telemetry and network calls: For an air-gapped workflow, check whether the extension attempts telemetry or other network requests. Continue’s offline guide specifically tells users to turn off “Allow Anonymous Telemetry,” select a local model in configuration, and restart VS Code. [Continue’s Ollama guide]
Continue’s documented sequence is to download and install its VSIX, disable anonymous telemetry, choose a local model in configuration, and restart VS Code. That is Continue-specific guidance; other extensions may use different settings and have different online dependencies. [Continue’s Ollama guide]
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What usually works—and what may not
| Workflow part | Offline expectation | Important qualification |
|---|---|---|
| Chat with a downloaded local model | Can work offline | Requires the runtime, model files, and editor integration to be installed and configured beforehand. [VS Code] [Ollama] |
| Inline suggestions in VS Code using a local model | Not supported by VS Code’s documented BYOK local-model route | VS Code says local models cannot currently provide inline suggestions. [VS Code] |
| Semantic search and embedding-dependent VS Code features | Still require online services | VS Code identifies these as GitHub-connected features. [VS Code] |
| Model, extension, or package downloads and updates | Generally need advance preparation | Keep required files and dependencies available locally; exact requirements vary by tool. |
| Telemetry | Depends on extension settings | Continue specifically advises disabling anonymous telemetry for offline use. [Continue] |
How local performance depends on your computer
There is no dependable universal minimum hardware specification without naming the model, its quantization, the context size, and the workload. A larger context window requires more memory. Ollama says its default context length is 4,096 tokens; its FAQ explains how to change the setting and inspect whether a model is running on the CPU, GPU, or both. If the model does not fit in available VRAM and uses system RAM, responses may be slower. Actual speed and usable context depend on the machine and configuration. [Ollama FAQ] [Ollama]
That trade-off affects more than response time. A tight context limit can restrict how much code and discussion the model can consider together; increasing it can raise memory demand. Test with the repository and prompt sizes you expect to use rather than choosing hardware from a model’s parameter count alone.
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How much coding quality to expect
Local coding quality varies by model and task. One useful but narrow data point is a 2025 preprint by Matotek, Cassel, Amiruzzaman, and Ngo, accepted to CCSC 2025. It evaluated eight locally hosted code models with 6.7–9 billion parameters on 3,589 Kattis programming problems. In that evaluation, the best local models had approximately half the acceptance rate of the proprietary Gemini 1.5 and ChatGPT-4 comparison systems. This measures performance on that contest-problem set and model lineup; it does not establish that local models are universally “half as good” for everyday coding. [2025 preprint]
For practical use, judge the model on the work you actually do: explaining a function, drafting a small edit, diagnosing a test failure, or navigating a larger repository. A local chat model may be useful even when it cannot replace a cloud-connected editor feature or match a stronger remote model on a particular benchmark.
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A quick offline-readiness check
- While connected, install the local runtime, download the intended model, and install the editor integration.
- Set the integration to use the local provider. Follow its own offline instructions; for Continue, disable “Allow Anonymous Telemetry,” select the local model, and restart VS Code. [Continue’s Ollama guide]
- Test chat and any tool actions you intend to use. Separately test features such as inline suggestions or semantic search; do not assume they use the same provider.
- Disconnect from the network and repeat the essential tasks. If something fails, identify whether it needs a missing model or dependency, a cloud-backed feature, or a network request such as telemetry.
- Check response speed and context behavior on your actual machine. In Ollama, its FAQ documents checking CPU/GPU placement and adjusting context length. [Ollama FAQ]
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