Key facts
| Runtime | Ollama, LM Studio or llama.cpp expose a local OpenAI-compatible API |
| Model class | Code-tuned 7B-14B models at 4-bit fit common developer machines |
| Editor integration | Point any OpenAI-compatible coding extension at the local base URL |
| Context | Send only the files and symbols a task needs |
| Latency | Small models respond fast; long contexts slow every request |
| Privacy | Code and prompts stay on the machine |
| Hosted fallback | Plugsky serves 30+ models over one OpenAI-compatible API |
| Endpoint status | Chat, streaming, tools, JSON mode and function calling are live |
TL;DR
- Local code models cover completion, explanation and test writing offline.
- Point an OpenAI-compatible editor extension at your local runtime.
- Send minimal context: the function, the types and the failing test.
- Evaluate with your own repository tasks, not generic benchmarks.
- Route large refactors to a hosted model when connectivity allows.
How it works, step by step
- Install a local runtime and pull a code-oriented model.
- Expose the runtime on its OpenAI-compatible endpoint.
- Configure your editor's AI extension to use that base URL.
- Start with small tasks: explain, rename, write a test, fix a type error.
- Tune context: include only relevant files and symbols.
- Track which prompts fail and add them to a local evaluation list.
- Add a hosted fallback for refactors that exceed local capability.
Try it yourself
Open the coding model selector →
What offline coding assistance covers
Local code models are strongest on bounded tasks: explain this function, rename this symbol, write a unit test for this branch, convert this snippet to another language, or suggest why a type error occurs. They are weakest on tasks that require holding an entire repository in mind, which is a context problem more than a model problem.
That split should shape how you use them. Keep interactive help local and narrow, and reserve wide-context work for a model with a larger window when you can reach one. A code-tuned 7B-14B model at 4-bit covers the local half well on a developer laptop.
Wiring a local code model into your editor
Most AI coding extensions can target any OpenAI-compatible endpoint. Start the local runtime, note its base URL and model name, and configure the extension to use them. Test with a single chat request before enabling inline completion, because completion changes the latency budget.
- Completion should be fast and short: a small model, small context, tight token limits.
- Explanation and tests can afford a larger model and longer context.
- Context selection matters more than model size: include the function, its types, call sites and the failing test.
- Memory limits how many models you can keep resident, so avoid loading several at once.
When to go hybrid
Offline coding help fails on three things: large refactors, unfamiliar frameworks and long files. Those are exactly the tasks where a hosted model with a larger context and stronger reasoning earns its place. The practical pattern is a hybrid: keep private or offline work local, and route the rest to an OpenAI-compatible endpoint.
Plugsky serves 30+ models behind one API, so editor configuration changes only the base URL and model name. Chat, streaming, tools, JSON mode and function calling are live; batch endpoints are coming soon, so keep bulk code generation on your current tooling until then. See pricing for plans and start free with plugsky-micro and plugsky-lite.
Honest comparison
| Concern | Offline local model | Plugsky hosted API | Check before deciding |
|---|---|---|---|
| Privacy | Code stays on the machine | Sent to your deployment | Repository sensitivity |
| Model choice | Limited to local memory | 30+ models on one API | Task difficulty mix |
| Latency | Fast for small models | Service dependent | Interactive versus batch work |
| Context | What you include manually | Larger context windows | Repository size |
| Offline | Works with no network | Needs connectivity | Travel and air-gapped work |
Frequently asked questions
Which local model is best for coding?
Choose a code-focused instruction model in the 7B-14B class at 4-bit, then test it on your repository. Models that share a family with larger coding models tend to behave similarly at smaller size.
How do I connect a local model to my editor?
Point your editor's OpenAI-compatible AI extension at the local server base URL, set a model name and test chat before enabling inline completion.
Can local models handle large repositories?
Not by loading everything. They work best when you supply a narrow slice: the function in question, its types, relevant call sites and the failing test.
Is offline coding help private?
Yes, if the runtime, the editor extension and any plug-ins do not send data out. Verify egress and disable telemetry.
How do I evaluate a local coding model?
Keep a list of real tasks from your repository with expected outcomes, and score compile success, test pass rate and review effort rather than style preference.
Should completions use a different model?
Often yes. A small fast model handles inline completion while a larger one handles explanations and refactors, but every extra resident model costs memory.
When should I use a hosted model instead?
For large refactors, unfamiliar frameworks and long-context work. Keep the local model for private or offline tasks and route the rest to an OpenAI-compatible endpoint.