A local LLM does not have internet access by default. If a model is running on your machine, inference is just local computation; internet access only appears when the surrounding app, API server, browser integration, or agent framework is allowed to connect outward, download files, or call tools.
That distinction matters because “runs locally” and “keeps your data local” are not the same claim. A local model can still sit inside an app that downloads models, checks for updates, exposes a REST API, connects across devices, or lets the model use web search and MCP servers; the model itself is offline, but the stack around it may not be.
The answer: a local model is not automatically online
Running a model locally means the weights are loaded on your own hardware and token generation happens on your own machine, as LM Studio explains in its basics documentation on downloading and loading models locally. That does not mean the model can somehow reach the web on its own. An LLM is not a tiny browser with opinions. It predicts the next token from the context it is given.
LM Studio states that it supports offline operation, and its docs separate local inference from network-dependent tasks such as discovering and downloading models. Ollama says the same thing in plainer terms: Ollama runs locally, while cloud models and web search are separate cloud features.
So the blunt answer is simple: a local LLM can answer from its training data and whatever text or files you feed it, but it cannot browse the internet unless some other layer gives it that ability.
Where internet access actually enters the stack
Internet access usually enters through the software wrapped around the model, not through inference itself. In practice, that means four common layers:
- Model management: searching registries, downloading weights, pulling updates, syncing settings, or fetching dependencies, as described in LM Studio’s offline docs and Ollama’s FAQ.
- App features: cross-device connections, remote access, or IDE integrations. LM Studio’s LM Link is a good example: it provides secure access from other devices and tools to a model that still runs locally.
- Agent tools: web search, API calls, filesystem actions, shell commands, and connectors to other services.
- MCP servers: external tool endpoints that let a model interact with resources beyond the prompt. LM Studio’s docs explicitly note support for MCP servers and a local REST API.
This is where a lot of the confusion comes from. People ask whether “the local model” has internet, when what they really mean is whether the application stack around the model can reach the network. Those are different questions, and the wrong one makes privacy claims sound cleaner than they are.
A useful mental model is the one in our piece on the local LLM stack: the model is one layer, the runtime is another, and the app or agent host is where tool permissions usually live. If that outer layer can make HTTP requests, the system can touch the internet even if the model weights never leave your laptop.
Privacy, agents, and hidden dependencies
Privacy depends less on whether the model is “local” and more on which other components can send data out. A fully offline setup can keep prompts, documents, and outputs on-device; LM Studio says offline mode supports local chat with already downloaded models, and only network tasks like model discovery or downloads need connectivity in the first place (LM Studio offline docs).
But once you enable agent features, the privacy story changes. A local app that can browse the web, call remote APIs, use MCP servers, or expose a local server to other machines is now handling data across boundaries. That does not make it bad. It does make “nothing ever leaves the machine” a claim that needs inspection, not applause.
The same issue shows up in enterprise products that market privacy while carving out operational exceptions; our look at Claude Enterprise privacy is basically this problem in a more expensive suit. And at the user level, hidden sharing paths are exactly how AI illiteracy data leaks happen: people think they are using one bounded tool, but the real system includes connectors, uploads, and side channels they never checked.
A local agent host makes this especially easy to miss. Our coverage of llama.cpp as a local agent host points to the same structural issue: local inference is not the same thing as a locally bounded capability set. Tool use is where the perimeter moves.
What can run fully offline? The core pieces are straightforward:
| Can run fully offline | Usually needs connectivity |
|---|---|
| Loading an already downloaded model locally (LM Studio basics) | Downloading new models (LM Studio offline docs) |
| Local text generation and chat (LM Studio offline docs) | Cloud models and web search in Ollama (Ollama Cloud) |
| Local API serving on your machine (LM Studio docs) | Remote APIs, web browsing, and online tool calls |
| Purely local file processing, if the app is configured that way | Cross-device or internet-exposed access such as LM Link |
The dry version is: the model is offline until you give the surrounding software a wire. After that, privacy depends on permissions, defaults, and whether the toolchain quietly pulls in online services.
Key Takeaways
- A local LLM does not browse the internet on its own; it only generates tokens from local weights and the context it receives.
- Internet access enters through the app, runtime, agent framework, MCP server, or cloud feature wrapped around the model.
- Local inference can run fully offline, but model downloads, web search, cloud models, and remote tool calls need connectivity.
- “Runs locally” is weaker than “nothing leaves the device”; privacy depends on the full stack and its permissions.
- Agent features are the main place hidden network dependencies appear in otherwise local AI setups.
Further Reading
- Offline Operation | LM Studio, What LM Studio can do offline and which features still require network access.
- Get started with LM Studio | LM Studio, How local model download, loading, and inference work in practice.
- LM Link | LM Studio, How local models can be accessed securely from other devices and tools.
- FAQ – Ollama, What runs locally in Ollama, plus how cloud features can be disabled.
- Cloud – Ollama, Ollama’s cloud-only features, including cloud models and web search.
References
- LM Studio, Offline Operation
- LM Studio, Get started with LM Studio
- LM Studio, LM Link
- LM Studio, Welcome to LM Studio Docs!
- Ollama, FAQ
- Ollama, Cloud
Last reviewed: 2026-06