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Yes—an AI coding assistant can work without an internet connection when its language model is downloaded to your computer or hosted on a local, on-premises server. Simply running an editor or agent harness locally is not enough: a local interface may still send prompts to a hosted model. Offline operation is therefore a deployment choice, not a property of the editor.
A local setup can provide chat, code explanation, file edits and agent-style tool use. It may not provide cloud-backed inline completions, semantic search or embedding-based codebase indexing. Your model choice, hardware, context window, tool support and organizational policies determine how close the offline experience is to a hosted service.
What does “offline AI coding assistant” actually mean?
Offline means that inference—the process that turns your prompt and code context into a response—runs on hardware you control, with no request sent to a hosted AI service. The editor, terminal and agent tools can also run locally, but those facts alone do not prove that inference is local.
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- Where do the tools run? On your workstation, a private server or a remote environment?
- Where does inference run? Inside a local runtime such as Ollama or llama.cpp, or on a provider’s servers?
Only the second answer determines whether prompts and code can be processed without a network connection. An air-gapped environment needs both local inference and local access to every dependency used by the workflow, including model files, extensions, package mirrors and source-control services.
How can I run an AI coding assistant completely offline?
The simplest documented route is a bring-your-own-key (BYOK) local model. VS Code documents connecting its chat experience to a local provider such as Ollama; that arrangement can work completely offline without a GitHub account or Copilot plan.
Set up a local provider
- Install a local inference runtime. Install Ollama, or another local provider approved by your organization, on the development machine or an internal server. Keep the runtime and model files on storage that remains available when the network is disconnected.
- Download a model while you have an approved connection. Model files can be large, so verify the download and record the model version and checksum if your security process requires it. After the model is present locally, disconnect the machine or block outbound traffic and confirm that the runtime still starts.
- Connect VS Code Chat to the local provider. In the Chat view, open the model picker and select the configured local model. The exact model list depends on the provider integration and the extension version; a model that appears in the provider does not automatically support every chat or agent feature.
- Configure utility models if needed. VS Code can use separate models for tasks such as generating chat titles or commit messages. Assign local utility models as well, or disable those conveniences so the editor does not attempt a hosted request.
- Test with networking disabled. Ask for an explanation of a small local file, request a bounded edit and run the resulting tests. Check your firewall, proxy and runtime logs for unexpected outbound connections before treating the setup as air-gapped.
This path gives you control over the model and the network boundary. It also means you are responsible for model downloads, runtime updates, storage, hardware capacity and safety review.
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There is no single yes-or-no answer because the answer differs between a local BYOK model, an experimental Copilot-managed on-device model and Copilot CLI with a local provider. A local BYOK model can operate without a Copilot plan, while the managed on-device option requires sign-in and unusually powerful hardware.
Copilot-managed on-device model
VS Code documents an experimental model served on the device through llama.cpp. The current instructions require all of the following:
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- A GitHub sign-in.
- An NVIDIA GPU with at least 96 GB of VRAM.
- Approximately 55 GB of free disk space for setup.
- A compatible llama.cpp build.
The documented model download is 52.8 GB, and the setup documentation specifies 54.8 GB of free space for the installation process. These figures describe the documented model and setup, not a universal requirement for every local model. The on-device feature is experimental, and the documentation warns that only one such model is loaded at a time; switching models can therefore involve unloading and reloading a large file.
Copilot CLI with a local model
GitHub announced local-provider support for Copilot CLI on April 7, 2026. Supported local-provider examples include Ollama, vLLM and Foundry Local. Setting the environment variable below prevents the CLI from contacting GitHub servers and disables telemetry:
COPILOT_OFFLINE=trueUse the variable together with a local model and an approved provider configuration. The selected model must support tool calling and streaming; GitHub recommends a context window of at least 128,000 tokens for the best results. A model that can only produce plain text may answer questions but fail when the CLI needs to inspect files, run commands or apply an edit.
Which features disappear when the model is local?
Offline chat and agent tasks do not reproduce every cloud feature. VS Code states that semantic search, inline suggestions (code completions) and features that rely on embeddings require the GitHub Copilot service and are unavailable through the documented BYOK local-model path.
| Capability | Local BYOK model | What the documentation establishes |
|---|---|---|
| Chat, explanations and generated code | Generally available when the chosen model and integration support them | Coverage depends on the model and extension |
| File edits and agent tool use | Available when the model supports tool calling and the harness permits the tools | Copilot CLI requires tool calling and streaming |
| Inline code completions | Unavailable in the documented BYOK offline path | Requires the hosted Copilot service |
| Semantic code search | Unavailable in the documented BYOK offline path | Requires the hosted service |
| Embedding-dependent features | Unavailable in the documented BYOK offline path | Require hosted embeddings |
| Vision, reasoning and other advanced abilities | Model-dependent | Local models do not necessarily match built-in models |
Feature availability can also vary by IDE and entry point. An editor extension, a terminal CLI and an internal agent service may expose different capabilities even when they use the same underlying model.
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What hardware does offline coding require?
Hardware needs scale with model size, quantization, context length and concurrency. Smaller models can run on ordinary developer machines but may be slower or less capable than hosted models. Larger models need more RAM or VRAM, faster storage and sustained cooling; long prompts consume additional memory.
The clearest published threshold is the experimental Copilot-managed model’s requirement for an NVIDIA GPU with at least 96 GB of VRAM and roughly 55 GB of free disk space. A local-provider setup is not bound to that threshold, but it still needs enough memory to load the selected model and its context. If the runtime begins swapping to disk, response latency can become impractical.
Plan capacity before downloading
- Model storage: reserve space for the model, temporary download files and future versions. The 52.8 GB on-device download and 54.8 GB setup requirement illustrate why a nearly full system drive can fail even when the final model would fit.
- Memory: account for the model weights, runtime overhead and the conversation or code context. A 128k-token context recommendation for Copilot CLI is a quality target, not a promise that every machine can hold that much context quickly.
- GPU or CPU: GPU acceleration can reduce latency, but a compatible GPU is not guaranteed by the fact that an editor runs locally. Check the provider’s supported hardware before selecting a model.
- Thermals and power: sustained generation can keep a workstation at high load. In a sealed or remote rack, cooling and power limits become operational constraints rather than merely performance concerns.
What are the main limitations of local coding models?
Capability varies by model
BYOK removes the requirement to use one provider’s model, but that flexibility creates uneven results. Tool calling, vision, reasoning quality, context handling and code-generation accuracy differ between models and even between quantized builds of the same model. Test the exact model and runtime you intend to standardize rather than assuming that a local model will behave like a hosted default.
Performance and scale
Hosted services can spread inference across specialized hardware and scale for many users. A local installation has the resources of one workstation or server. Smaller models may answer quickly but need more correction; larger models may produce better plans while taking longer and limiting how many developers can use the system concurrently.
Maintenance burden
You must patch the runtime, manage model versions, monitor disk usage, replace failed hardware and keep integrations compatible with the editor or CLI. In an air-gapped network, updates require a controlled transfer process, malware scanning and an internal package or model repository.
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Safety and trust
VS Code cautions that local models may not use the same safety filters as GitHub-hosted models and can be more susceptible to malicious instructions, especially when actions are auto-approved. Its BYOK documentation says responsible-AI filtering is not guaranteed. Treat generated shell commands, dependency changes and file deletions as untrusted until reviewed.
Policy and account boundaries
Local BYOK models can work without a GitHub account or Copilot plan. In contrast, the Copilot-managed on-device model requires GitHub sign-in. Business and Enterprise administrators can restrict BYOK through policy, so an individual developer’s successful local setup may not be permitted in a managed organization.
Which offline approach should I choose?
Choose based on the boundary you need, the hardware you have and the features you can give up.
| Option | Account requirement | Hardware and setup | Feature coverage | Network and data behavior | Best fit |
|---|---|---|---|---|---|
| Local-provider BYOK (such as Ollama) | No GitHub account or Copilot plan required | Choose your own model and runtime; capacity varies | Chat, explanations and agent tasks when supported; no documented offline inline suggestions, semantic search or hosted embeddings | Can remain entirely local after model installation | Teams wanting maximum model and network control |
| Copilot-managed on-device model | GitHub sign-in required | Experimental llama.cpp path; NVIDIA GPU with at least 96 GB VRAM and about 55 GB free disk space | Integrated experience, but only one on-device model is loaded at a time | Model runs on the device; verify which surrounding features still call hosted services | Users who meet the strict hardware requirement and want an integrated model |
| Copilot CLI with a local provider | Depends on the CLI configuration; offline mode is explicit | Ollama, vLLM or Foundry Local; model must support tool calling and streaming | Terminal-based agent workflows; 128k-token context is recommended for best results | COPILOT_OFFLINE=true prevents GitHub-server contact and disables telemetry |
Air-gapped terminal work and scripted development tasks |
How do I make an air-gapped setup safer and more reliable?
- Define the boundary. Decide whether “offline” means no public internet, no outbound traffic at all or merely that source code cannot leave the premises. Document exceptions for source control, package mirrors and update imports.
- Stage dependencies before isolation. Transfer the model, runtime, editor extensions and required packages through your organization’s approved scanning process. Keep version records so a rebuild does not depend on an unavailable download.
- Disable accidental fallbacks. Configure the editor or CLI to use the local model explicitly, set
COPILOT_OFFLINE=truefor Copilot CLI, and remove hosted models from automatic selection where policy permits. - Verify network behavior. Disconnect the network or apply an egress-deny rule, then perform chat, tool-use and error-path tests. Inspect firewall and runtime logs for attempted connections.
- Control agent permissions. Require confirmation before shell commands, file deletion, dependency installation or broad rewrites. Local inference does not make generated instructions trustworthy.
- Measure representative work. Test the languages, repository sizes, context lengths and tools your team actually uses. Record latency, failed tool calls, correction rate and memory use before approving a model.
What should I troubleshoot when offline assistance fails?
- The model is not listed: confirm that the provider is running, the model is downloaded locally and the editor extension recognizes the provider version.
- Chat works but edits fail: check tool-calling support and whether the harness has permission to read or modify the workspace.
- Responses stop on long files: reduce the selected context, split the task or use a model with a larger context window. Copilot CLI documentation recommends 128k tokens for best results, but available memory still limits practical throughput.
- The machine becomes unresponsive: inspect RAM and VRAM use, reduce concurrency, choose a smaller model or move inference to an internal server.
- The editor still tries the network: review model, telemetry and extension settings, then enforce outbound firewall rules. A local harness alone does not prove local inference.
Is offline coding assistance worth the trade-off?
Offline assistance is worthwhile when code or prompts cannot leave a controlled environment, when connectivity is unreliable, or when an organization needs predictable local operation. The trade is that you exchange hosted capacity and cloud features for model management, hardware costs, slower scaling and responsibility for safety controls.
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For many developers, a local-provider BYOK setup is the most accessible starting point: it can deliver private chat and bounded agent work without a Copilot plan. If inline completions, semantic search or embedding-based indexing are essential, the documented offline path will not replace the hosted service. Evaluate those missing features explicitly rather than assuming that a local model provides a complete cloud-equivalent experience.
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Frequently Asked Questions
Can I use an AI coding assistant on a laptop with no dedicated GPU?
Some smaller local models can run on CPU, but response speed and context capacity depend on available system memory and the model’s size. The documented Copilot-managed on-device model is a separate case and requires an NVIDIA GPU with at least 96 GB of VRAM.
Will offline prompts ever leave my company network?
They should not when inference, model files and supporting services are local and outbound traffic is blocked. Verify the editor, extensions, telemetry settings and package integrations individually; a local agent harness can still call a hosted model unless configured otherwise.
Can an offline model search my whole repository?
It can inspect files that the local harness is allowed to provide, but the hosted semantic-search and embedding features documented for the Copilot service are unavailable in the BYOK offline path. You may need local text search or a separately deployed indexing system.
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Local execution does not guarantee the same safety filtering as a hosted service. Require approval for shell commands and destructive edits, review diffs, and test changes in an isolated workspace before merging them.
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