Keep your Swift code on your own machine. This step-by-step guide shows how to run Continue with a local model via Ollama for private, offline-friendly iOS development.
One of Continue's best features is that it can connect to a model running on your own computer. For iOS developers with proprietary code, that is a meaningful privacy advantage.
With a local model, your Swift source does not leave your machine to reach a cloud API. That helps with confidentiality requirements and lets you keep working when you are offline.
The trade-off is that local models depend on your hardware. A laptop will run smaller models comfortably, while larger models want more memory and a capable GPU.
This guide uses Ollama, a popular and well-documented local model runner, as the backend. The same Continue principles apply if you use another local runtime that Continue supports.
Start by installing Ollama from its official website for your platform. On a Mac, this is the same machine where Xcode and your simulator live, which keeps the whole loop local.
After installing, confirm the runner is available from your terminal. Ollama runs a local service that Continue will talk to over your own machine's network interface.
Keep the runner updated as you go. Local tooling evolves quickly, and staying current avoids compatibility surprises with newer models.
Before pulling a model, glance at your machine's available memory. That number is the main constraint on which models will run smoothly for you.
Use the runner to download a model suited to coding tasks. Ollama's model library lists options, including smaller models that fit modest hardware and larger ones for stronger results.
Start small if you are unsure. A lighter model gives you fast, responsive autocomplete and lets you validate the whole setup before committing to something heavier.
Remember that Swift may not be the most represented language in every model's training. Test the model on real Swift snippets from your project to judge whether its output is useful.
You can keep multiple models installed and switch between them. This lets you pair a fast model for inline completion with a larger one for deeper chat questions.
Open Continue's configuration in VS Code. You will add your local runner as a provider and name the specific model you pulled.
Continue's documentation shows the exact configuration format for local providers, including the local endpoint and model name fields. Follow those docs closely, since the keys must match precisely.
Save the configuration and reload the Continue panel. The local model should now appear as a selectable option in chat and, if you assign it, for autocomplete.
Because everything runs locally, there is no API key to manage here. Your credentials are simply not part of the equation, which is part of the appeal.
Continue lets you assign different models to different roles, and local setups benefit from this. Use a small, fast local model for autocomplete so suggestions stay snappy.
For chat and multi-step reasoning, you might select a larger local model that you accept will be slower. The quality-versus-latency trade-off is yours to tune.
If your hardware struggles with larger local models, consider a hybrid approach. You can keep autocomplete local for privacy and route heavier chat to a hosted model when the code is not sensitive.
Document whichever split you choose so your team is consistent. Predictable behavior across a team matters more than squeezing out the last bit of speed.
Open a Swift file from your actual project and try inline autocomplete. Type a partial function or property and see whether the local model completes it sensibly.
Then open chat, highlight a block of Swift, and ask for an explanation or a refactor. This exercises the model's understanding of your code, not just its ability to guess a line.
Evaluate output critically. Local models vary widely, and a smaller one may need clearer prompts and more selected context to be genuinely helpful.
If results are weak, first try a larger model or sharper prompts before concluding local is not for you. Prompt quality and context selection often make the difference.
As with any Continue setup, the assistant stops at writing and reasoning about code. Building and shipping still happen in Xcode.
After your local model helps you draft or refactor Swift, switch to Xcode and build against the iOS SDK. Local AI does not change how Apple compiles, signs, or distributes your app.
Handle provisioning, capabilities, and signing in Xcode using your Apple Developer Program membership. None of that is available from a local model or from VS Code.
Archive and upload through Xcode when you are ready for TestFlight or the App Store. Your private local workflow ends at the editor; the release path is Apple's.
Running a model locally is a strong privacy posture, but it helps to be precise about what it does and does not guarantee. During inference, a local model processes your Swift on your own machine rather than sending it to a cloud API.
That covers the model call itself. It does not automatically cover other tools in your editor, so if you also run a cloud-based extension alongside Continue, that separate tool may still transmit code.
For genuinely confidential work, confirm the behavior of every component in your setup, not just the language model. Read the official documentation for Continue and for your chosen runner rather than assuming a default.
The payoff is real for teams under compliance constraints. A fully local path means proprietary Swift never has to traverse a third-party service, which is difficult to achieve with cloud-only assistants.
Local models are files on disk, and a coding setup can accumulate several of them quickly. Before you pull a second or third model to compare, check that you have room, since larger models take real space.
Adopt a simple hygiene habit. Remove models you have decided against so your machine is not cluttered with backends you never select in Continue.
Models and runners also improve over time, so revisit your choices periodically. A model that felt weak on Swift a while ago may perform better in a newer release, and trying the latest option costs only a download.
Keep a short note of which model you assigned to autocomplete and which to chat. When you update or swap one, you will know exactly where to change the reference in Continue's configuration.
Local models need occasional care. Periodically update your runner and try newer models, since improvements arrive frequently and can noticeably raise quality.
Watch your disk usage. Models take real space, so remove ones you no longer use to keep your machine tidy.
Re-benchmark on your own Swift codebase after any significant change. Your project is the only benchmark that truly matters for your day-to-day experience.
Finally, keep the privacy benefit in perspective. Confirm exactly how your chosen model and runner handle data, and rely on official documentation rather than assumptions when confidentiality is on the line.
Running a model locally means your code does not go to a cloud API during inference. Always verify the specific runner and model's data behavior in their official docs, especially for confidential projects.
It depends on the model size. Smaller models run on typical laptops, while larger ones want more memory and a strong GPU. Start with a small model and scale up as your hardware allows.
Yes. A common pattern is local autocomplete for privacy and speed, with a hosted model for heavier chat when the code is not sensitive. Continue lets you assign models per role.
No. Ollama is one popular option, but Continue supports other local runtimes as well. Check Continue's documentation for the full list of supported local providers.
Yes. No AI model, local or hosted, builds, signs, or submits an iOS app. Xcode and the Apple Developer Program are still required to release.