Cursor vs Continue: Which Coding Assistant Works Offline?

Continue is generally the better choice if you need an AI coding assistant to work offline: it is an open-source extension for editors such as VS Code and JetBrains IDEs that can connect to models running on your own computer. Cursor can use local models in some workflows, but it is a more tightly integrated editor whose features and model access are substantially oriented around its online services.

What “offline” means for a coding assistant

An assistant is not fully offline merely because the editor opens without internet. The model that generates suggestions must also be available locally, and the assistant must be able to send it the relevant code without calling a remote service. Some features may still depend on a network even when local chat or completion works.

It helps to separate three situations:

• Offline editing: You can open, change, and save files without a connection. Both editors can do this as ordinary code editors.
• Local AI inference: Prompts and code are processed by a model on your machine. Continue supports this through compatible local model providers.
• Cloud-backed assistance: Prompts or code are sent to a provider’s hosted model. This usually requires internet access and may be subject to the provider’s data handling terms.

For this comparison, “works offline” means the assistant can provide useful AI help with no internet connection—not simply that its editor remains usable.

How Continue works with local models

Continue is an open-source coding assistant available as an extension for VS Code and JetBrains IDEs. It can connect to hosted models, but it can also be configured to use a model served on the same computer or on a reachable local network. Common local-model setups use a model runner or inference server; Continue acts as the editor-side client.

That arrangement can support tasks such as chatting about selected code, asking for explanations, and generating or editing code, depending on the model, provider configuration, and features enabled. Autocomplete is a separate capability: it needs a model and settings suited to fast completion, and not every model that handles chat well will provide a good completion experience.

To prepare for offline use, install the editor extension, model runner, and model files while you still have internet access. Configure Continue to use the local provider and model, then test it by disconnecting the network. Downloading a model can require substantial storage and bandwidth; after it is downloaded, inference can run locally, although speed depends on the computer and model size.

How Cursor compares offline

Cursor is a code editor based on the Visual Studio Code ecosystem, with AI features built into its editing experience. Its core editor can be used without a connection, but many prominent assistant workflows are designed around Cursor’s cloud-connected services and supported remote models.

Cursor has offered options for configuring certain models locally, but that should not be confused with every Cursor feature being available offline. Local model compatibility and the available behavior can depend on the specific model and Cursor release. Features that rely on hosted inference, account services, or remote context processing will not work as usual when the computer has no network access.

So the practical distinction is not that Cursor can never use local inference. It is that Continue is usually the more flexible starting point when local model choice and an offline-first setup are the main requirements. Cursor may be more convenient for someone who primarily wants an integrated editor and normally has reliable internet.

Setup, hardware, and capability trade-offs

Local inference trades network dependence for hardware demands and setup work. A model has to fit the available memory, and larger or more capable models can be slower or impractical on a modest computer. Performance also depends on the model runner, quantization, processor, memory bandwidth, and whether suitable GPU acceleration is available.

Expect differences from a strong hosted model. A smaller local model may miss project-specific context, produce less reliable edits, or struggle with complex debugging. The editor can supply files and instructions, but local inference does not automatically make a model understand an entire codebase. You may need to select relevant files or configure context features, and those features must themselves be compatible with offline operation.

A sensible offline test is to try the exact work you expect to do:

1. Install the editor, extension, local model runner, and model before disconnecting.
2. Confirm the model loads and answer a simple coding question in the editor.
3. Try autocomplete and an edit or chat task separately; success in one does not guarantee the others work.
4. Turn off network access and repeat the tests, watching for features that wait for a cloud service.
5. Check that the model is using local files and that no remote provider is selected for the task.

Privacy and practical limitations

Using a local model can reduce the need to send source code to a hosted inference provider, which can matter for proprietary projects or restricted environments. It is not an automatic privacy guarantee. Verify the extension’s configuration, telemetry and indexing settings, model-provider selection, and any other tools that may communicate over the network. In a managed workplace, follow the organization’s policies rather than assuming local use is permitted.

Offline also means missing updates and remote resources. Documentation lookup, hosted model access, account functions, and features that retrieve or process information online may be unavailable. Keep installers and model files available if you need to reproduce the setup on a disconnected machine, and check the current product documentation for feature-specific requirements because these can change between releases.

Which one should you choose?

Choose Continue if your priority is running an assistant with a model stored and served locally, retaining control over model selection, or working in a disconnected environment. Its open-source extension and provider-based setup make local-model configuration a natural use case, though you still need to assemble and maintain the local model stack.

Choose Cursor if you value its integrated editor experience and expect to use internet-connected AI features most of the time. Consider it only after confirming that the particular local-model workflow you need works in your installed version; do not assume that all features become offline simply because a local model option exists.

If you only need to edit code without AI, either editor can be used offline for basic file editing once installed. If you need dependable offline AI, make the choice based on a real disconnected test with your model, hardware, and tasks—not on a general claim that an editor supports local models.

Frequently Asked Questions

Can Continue work with no internet?
Yes, when Continue is configured to use a local model that is already installed and running. Install and configure the required software and model before going offline, then test the features you intend to use.

Can Cursor run a local model?
Cursor has supported local-model options for certain workflows, but availability and behavior can vary. A local model does not make cloud-dependent features work offline, so verify the exact feature in your current installation.

Does a local AI coding assistant send my code to the cloud?
Local inference can keep the model request on your machine, but the answer depends on configuration and other enabled services. Check provider settings, telemetry, and network behavior; do not treat “local model” as proof that all data stays local.

Will Continue or Cursor work on a computer with no GPU?
Local models can run on some CPU-only systems, but performance and the models that fit may be limited. Requirements depend on the model and inference software, so check their current guidance and test on the target computer.

Which is better for coding without internet?
Continue is generally the stronger fit for offline AI because local-model configuration is central to its flexibility. Cursor can still be useful offline as an editor, but its AI experience is more dependent on online services.

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