Yes. A local large language model (LLM) coding assistant can continue to chat about code, edit files and run supported local workflows with no internet connection after you have prepared it. The offline setup must include the editor, extension, runtime, model files and configuration. Internet-dependent features—such as downloading another model, updating software, cloud models, web search and remote tools—will stop working once the machine is disconnected.
The important distinction is between offline inference and offline maintenance. The model can generate responses entirely on your computer, but obtaining new models, runtimes or application updates still requires a connection or a deliberate sideloading process.
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What “completely offline” means for a coding assistant
Offline operation means a request from your editor is processed by a model running on the same computer, or on another server you control on the local network. The prompt, source code and response do not need to travel to a hosted AI service. LM Studio documentation says that, after model files are present, it can operate entirely offline and that material entered while chatting with its local LLMs does not leave the device. Microsoft’s Visual Studio Code documentation likewise states that a local model can be used completely offline.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThis does not make every button in an AI-enabled editor independent of the internet. A local model cannot provide a cloud service’s web search, and an extension cannot fetch a package from a remote registry when the network is blocked. Treat each feature as a separate dependency rather than assuming that “local model” makes the whole application offline.
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Offline inference
Inference is the actual generation of an answer, code completion or edit from model files already stored on your machine. It can continue after you unplug Ethernet, disable Wi-Fi or place the computer in an air-gapped environment. A local HTTP endpoint on localhost, or a server bound to your private LAN, can receive requests without an outside connection.
Offline software maintenance
Model catalogs, downloads, runtime downloads, automatic update checks and extension installation normally contact the internet. Plan those actions before disconnecting. If you need a different model later, you must bring its files into the isolated environment by an approved transfer method, such as removable storage, and then import or sideload them.
What to download and configure before disconnecting
Prepare the entire chain while the computer is online. A missing editor extension or runtime is not something an air-gapped assistant can repair for you.
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- Install the editor. Install the required version of Visual Studio Code or your chosen development environment, including any language extensions and local project dependencies that your normal workflow needs.
- Obtain the integration package. For an air-gapped Continue setup, its documented sequence is to download the latest VSIX while connected, install that VSIX in Visual Studio Code, turn off Allow Anonymous Telemetry in user settings, select a local model in
config.yaml, and restart Visual Studio Code. - Install the model runtime. Install the local runtime—such as Ollama or the runtime bundled with a desktop local-model application—before entering the isolated network. Download any runtime components it needs while online.
- Acquire model files. Model search and downloads require connectivity. Download the model or models you intend to use, or copy approved files into the environment later. LM Studio explicitly supports sideloaded model files after they have been obtained elsewhere.
- Configure the endpoint. Point the extension or editor provider at the local endpoint rather than a hosted API. If the provider offers a host or port setting, verify that it resolves to the local machine or your controlled LAN server.
- Prepare local project resources. Download language servers, formatters, linters, documentation and package caches that your offline workflow will call. A model may be local while a code action still tries to retrieve a missing package.
- Test before isolation. Run a chat prompt, an edit request and any agent or tool workflow you expect to use. Then disable the network temporarily and repeat the tests, watching for requests that fail or trigger a sign-in prompt.
How Ollama, LM Studio and Continue with Visual Studio Code differ
These choices overlap, but they solve different parts of the stack. Ollama is primarily a local model runtime and server. LM Studio combines model management, a desktop chat interface, document retrieval and a local server. Continue is an editor extension that can use a configured local model; Visual Studio Code can also connect directly to local providers. The table describes offline-relevant behavior rather than claiming that one tool is universally best.
| Option | Installation and model management | Editor and interaction support | Agent, tool and cloud considerations | Offline controls and server use |
|---|---|---|---|---|
| Ollama | Install the runtime while online and obtain model files before disconnection. New model acquisition and runtime updates are unavailable without a prepared transfer. | Provides a local endpoint that an editor integration can call. Chat and other capabilities depend on the model and the client connected to it. | Local generation is possible, but an editor’s web search, remote tool, cloud model or package-registry step remains online-dependent. | Designed to serve models locally; keep the endpoint on the machine or trusted LAN and verify the client is not configured for a hosted provider. |
| LM Studio | Model search and downloads need connectivity; downloaded or sideloaded files can be used afterward. The application and runtime should be installed before entering an air gap. | Desktop chat, local document conversations and local RAG can continue. Its OpenAI-compatible local requests stay on the local machine when directed to the local server. | Document processing is performed on the machine. External search, cloud models and remote tools still require their respective services. | Can run entirely offline once model files are available. Its documentation says that text entered while chatting with local LLMs does not leave the device. |
| Continue with Visual Studio Code | Download the VSIX online, install it offline, disable Allow Anonymous Telemetry, and select a local model in config.yaml. |
Supports local-model chat, edits and agent workflows, subject to the selected model and extension configuration. Autocomplete quality and availability depend on that configuration. | Any configured cloud model, web-search tool, remote MCP server or package registry breaks the air-gapped promise. Visual Studio Code semantic search and Copilot inline suggestions require GitHub/Copilot services. | Runs against a local endpoint after configuration. The VS Code integration documentation lists version 1.127 or newer for its Ollama integration. |
“Works offline” therefore describes a configured path, not a product-wide guarantee. A Continue chat connected to a local model can be offline while a second profile in the same editor points to a cloud model. Keep separate profiles or disable cloud features when isolation is required.
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There is no single universal minimum because model size, quantization, context length and the speed you expect change the requirements. The following figures are vendor recommendations, not benchmark results or hard guarantees.
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| Platform | Documented guidance | Practical implication |
|---|---|---|
| Apple Silicon Mac | LM Studio recommends 16 GB or more of RAM. An 8 GB Mac may work with smaller models and modest context sizes. | Use a smaller model and shorter context on 8 GB systems; long files and multiple concurrent sessions will put more pressure on memory. |
| Windows x64 or ARM | At least 16 GB of RAM is recommended; at least 4 GB of dedicated VRAM is recommended. x64 systems require AVX2 support. | Check the processor instruction set and available dedicated graphics memory before choosing a model. Integrated graphics may still run some configurations, but the cited guidance specifically recommends dedicated VRAM. |
| Linux | Supported platforms include x64 and ARM64 systems running Ubuntu 20.04 or newer. | Confirm that the runtime and editor packages support your distribution and architecture before you disconnect. |
Model files can consume substantial disk space, and an external SSD is an optional way to carry them for sideloading when internal storage is limited. Neither an SSD nor a RAM upgrade is required for every setup; compatibility with your particular computer must be checked before purchasing hardware.
Setting up an air-gapped workflow
Ollama as the local runtime
- Install Ollama and its required components while online.
- Download the model you intend to use and verify that the runtime can load it.
- Start the local server and record the local endpoint used by your editor integration.
- In Visual Studio Code or another client, choose the Ollama/local provider rather than a cloud provider. The Ollama integration documentation requires Visual Studio Code 1.127 or newer.
- Send a small prompt and a code-edit request while connected, then disconnect and repeat them. A successful response with no external sign-in or network error confirms the basic path.
The runtime does not make editor features local by itself. Keep cloud autocomplete, remote search and remote tool settings disabled if the machine must remain isolated.
LM Studio for chat, documents and a local API
- Install LM Studio and download or sideload the model files while online.
- Load the model and test ordinary chat, including a prompt that references a local document if you rely on document Q&A or local RAG.
- Start its local server when your editor needs an API. Use the application’s OpenAI-compatible local endpoint and configure the editor to use that address.
- Disconnect the network and test both direct chat and an editor request. Document processing should remain on the machine; a failure usually indicates that the client selected a cloud model or an unavailable external tool.
Continue in an air-gapped Visual Studio Code installation
- While online, download the latest Continue VSIX and any model files required by your chosen local runtime.
- Install the VSIX in Visual Studio Code before isolation.
- Open user settings and turn off Allow Anonymous Telemetry.
- Edit Continue’s
config.yamlto select the local model and endpoint. - Restart Visual Studio Code, test chat, edit and agent actions, and then repeat those tests with the network disconnected.
Continue’s local workflows are conditional: the selected model must support the requested operation, and every tool in the workflow must be available locally. An agent that calls a remote MCP server or a package registry is not an offline agent even if its language model is local.
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What continues to work without Wi-Fi
- Chat with a locally loaded coding model.
- Generation, explanation, refactoring and edits handled by that model.
- Local document chat and retrieval-augmented generation in LM Studio, because document processing stays on the machine.
- Requests to a local server on
localhostor a controlled private LAN. - Visual Studio Code chat and utility tasks when a local or bring-your-own-key setup is correctly configured.
- Continue’s local-model chat, edit and agent workflows when their configuration and tools are local.
What stops working or needs a prepared substitute
- Model discovery and downloads: catalogs and new model files cannot be fetched. Sideload a file that was obtained earlier.
- Runtime changes: downloading, hot-swapping or updating runtime components requires an offline package or a later reconnection.
- Application updates: built-in update checks cannot complete.
- Cloud models: a profile that points to a hosted API will fail or wait for a network response.
- Visual Studio Code semantic search and Copilot inline suggestions: these functions depend on GitHub/Copilot services rather than only the local model.
- External tools: web search, remote MCP servers, package registries and any API called by an agent are unavailable unless mirrored locally.
Does offline use keep source code on the machine?
For a request routed solely to a local model and local endpoint, the model inference occurs on your device. LM Studio specifically states that nothing entered while chatting with its local LLMs leaves the device. That statement does not cover every extension, crash reporter, language service or tool in your editor. Disable anonymous telemetry where the extension provides that control, audit provider settings, and remove cloud, web-search and remote-tool integrations when code must not leave the environment.
A local-network server also changes the boundary: the request may leave the workstation for another machine you control. That can still satisfy an offline or air-gapped policy, but it is not the same as “this computer only.” Restrict the server to the intended interface and treat LAN access as a security decision.
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The editor says the model is unavailable
Confirm that the runtime process is running, the endpoint address and port match the editor configuration, and the model is loaded locally. Reconnect only to obtain missing files; do not assume an editor retry will download them silently.
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Chat works but autocomplete or semantic search does not
Those are separate features. Visual Studio Code’s semantic search and Copilot inline suggestions require GitHub/Copilot services. Continue’s edit and agent functions depend on its model configuration, while an autocomplete provider may be configured independently. Use local chat and editing, or install a genuinely local completion component before isolation.
An agent stops at a tool call
Inspect the tool definition. Web search, a remote MCP server, a cloud API or a package registry cannot answer in an air gap. Replace it with a local tool, preload the needed package or documentation, or remove that step from the workflow.
The model is slow or crashes
Reduce context length, close other memory-intensive applications and choose a smaller model. The vendor figures above are recommendations, not guarantees; available RAM, VRAM, model size and quantization determine whether a particular configuration is usable.
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A reliable way to verify the air gap
- Record the exact local endpoint, model name and editor profile that worked while connected.
- Disable Wi-Fi and unplug network cables, or apply the organization’s air-gap controls.
- Run a short code explanation, a bounded edit and one local-document query if applicable.
- Try one operation that should fail, such as model search, to confirm that no hidden fallback is masking the configuration.
- Review logs and settings for cloud providers, telemetry, remote MCP servers and external package calls.
- Document the files and installers needed to restore the setup after a machine rebuild.
If the local prompts and edits succeed under those conditions, the core coding assistant is operating offline. Future model additions, runtime updates and cloud-dependent conveniences will still require a controlled online process or a prepared offline transfer.
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