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You can talk to a local model through a desktop chat app, a terminal, a browser, an API, a programming library, an IDE, or a custom application. These are not nine separate kinds of model: a single local runtime can expose several interfaces, so you can start with a chat window and later connect the same setup to a script or coding tool.
The right starting point depends on what you want to do. Choose a desktop app for visual model discovery and management, a command line for scripting and direct control, a browser interface for web-based access, or an API or SDK when another program needs to send prompts to the model.
What are the nine ways to talk to a local model?
A local model is the language model running on your own computer or on a server you control. The runtime loads and operates the model; the interface is the place or method you use to send it a prompt and receive a response. Keeping those two roles separate makes the options easier to understand: you can change the interface without necessarily changing the model.
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These nine options overlap. For example, one runtime may provide a terminal command, a local HTTP endpoint, and a browser chat page. An IDE or a Python script can then act as another client of that endpoint. The choices below describe ways of interacting, not nine products that must be installed together.
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1. A desktop chat application
A desktop application is the most visual route. LM Studio, for example, presents a workflow for finding, downloading, managing, and chatting with local models. That can make it a natural first choice if you want to browse model options and have a conversation without building an application or learning a command-line workflow.
Its role need not stop at chat: LM Studio also documents a command-line interface, APIs, software development kits (SDKs), and integrations. That combination can suit someone who wants a graphical starting point but may later connect the runtime to code. A desktop interface is not automatically the best fit if your priority is automation, remote browser access, or a shared multi-user workspace; those needs point toward other surfaces.
2. A terminal command-line interface
A command-line interface (CLI) lets you run or talk to a model from a terminal. Ollama documents command-line model use, and llama.cpp provides llama cli for direct terminal conversations. This route is useful when you already work in a terminal, want to put model interaction into scripts, or prefer a compact interface over a graphical application.
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3. A browser page served by the runtime
A browser chat page can be served by the local runtime itself; opening a model in a browser does not, by itself, mean the conversation is being sent to a cloud service. llama.cpp documents llama serve as an OpenAI-compatible server with a built-in web interface. It offers a browser-based conversation surface while keeping the runtime in the local-service role.
This option is distinct from a separately installed web platform: the interface is built into the server workflow. It can be convenient when you want to interact through a browser rather than a terminal, but it is still important to understand where the server is running and who can reach it. A local interface and a remotely reachable service are not the same thing.
4. A self-hosted web platform
Open WebUI is a browser-oriented platform that can connect to Ollama and to local endpoints that use OpenAI-compatible APIs. Its documentation also describes support for multiple connection protocols, including OpenAI-compatible APIs and Open Responses. This makes it an option when you want a web interface that sits in front of a model-serving runtime rather than relying only on a runtime’s built-in page.
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A separate web layer can be relevant when browser access, knowledge bases, permissions, or team workflows matter. Those features introduce configuration and deployment decisions that a single-user desktop chat may avoid. In particular, distinguish “self-hosted” from “accessible only to me”: where the platform runs, how it is exposed on a network, and who is allowed to sign in all affect the actual setup.
5. A local HTTP or OpenAI-compatible API
An API is the bridge between a running model service and another program. Rather than typing into a chat window, an application or script sends a request to the service and receives a response. LM Studio documents a REST API for interacting with local models from apps and scripts; llama.cpp documents a local server with an OpenAI-compatible interface.
This is often the key option if you want to connect a notebook, a personal application, or a compatible client. “OpenAI-compatible” describes an interface convention that can help a client communicate with a server; it does not mean the model is hosted by OpenAI, nor does it guarantee every client feature will work identically. Check that the client supports the endpoint and capabilities your runtime provides.
An API also changes the practical question from “where do I chat?” to “which process can reach the endpoint?” A script on the same computer and a service exposed to other devices have different access boundaries. Follow the runtime’s guidance on endpoint configuration and network exposure rather than assuming an API is private just because the model is local.
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6. A Python integration
Python is a common way to put local-model calls inside notebooks, scripts, and applications. LM Studio documents a Python SDK, and Hugging Face’s local-app guidance distinguishes Python-library access from CLI and server approaches. Depending on the chosen runtime and library, your code may connect through an SDK or send requests to a local server.
Choose this route when the model is one component of a larger Python task—for example, when a program needs to prepare input, request a response, and then process the result. Before writing code, decide whether your setup is library-based or server-based. They are different integration patterns, and the instructions for one should not be treated as interchangeable with the other.
If you are using a notebook or an existing application, check which model endpoint it expects and whether it supports the runtime’s API format. A local model being available on the computer does not automatically make it discoverable to every Python package; the integration still needs a supported way to reach it.
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7. A TypeScript or JavaScript integration
LM Studio documents a TypeScript SDK for local workflows and plugins. A programming-language SDK can provide a structured way for JavaScript or TypeScript code to interact with a runtime, without making a terminal conversation the main user experience.
This path is relevant when you are building a JavaScript application or extending a workflow with a plugin. Confirm the SDK’s intended environment and supported features in its documentation. Do not assume a TypeScript SDK, a REST API, and a browser-facing web application all have the same setup requirements simply because they can ultimately reach the same model.
8. An IDE or coding assistant
An integrated development environment (IDE) can be the conversation surface when you want model assistance alongside code. Visual Studio Code documents local-model provider options, including setup guidance for Ollama and llama.cpp. Its documentation specifically directs users seeking local Ollama models to install the official Ollama extension from the Ollama publisher.
This is a developer-focused choice: it places interaction in the editor rather than a separate chat application. The extension or provider is the client, while the local runtime supplies the model. Verify that the IDE integration is configured for the runtime you actually use; installing an extension for one provider does not by itself install or configure every local model.
9. A custom or agent-based application
When an existing chat window or IDE is not the right shape, you can make a custom interface around a local runtime. APIs, SDKs, Model Context Protocol (MCP) connections, and coding-agent integrations can serve as building blocks for workflows that combine model responses with other software or tools. LM Studio, llama.cpp, and Open WebUI document parts of this broader integration landscape.
This is the most flexible category, but also the one that asks the most of the builder. You must choose the runtime, client connection, user interface, and any tool permissions or network boundaries yourself. Treat each connection as a separate design decision: a model request, an agent tool call, and a browser session may have different access to files, services, or users.
How should I choose an interface?
Start with the task you want the model to perform and choose the least complicated interface that supports it. A graphical app is a sensible starting point for exploration; a CLI fits terminal-centered work; an API or SDK is the practical foundation for code; and a browser or IDE interface makes sense when that is where you want to work.
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- For model discovery and ordinary conversation: begin with a desktop application that includes visual model management.
- For terminal work or repeatable commands: use a runtime’s CLI, such as Ollama’s documented command-line workflow or llama.cpp’s
llama cli. - For browser chat: decide whether a runtime’s built-in web page is enough or whether you need a separate self-hosted platform.
- For scripts, notebooks, and applications: prioritize a documented local API or an SDK for your programming language.
- For coding assistance: check which local provider your IDE supports and configure the matching extension or integration.
- For shared or agent-based workflows: plan the deployment, permissions, and connections in addition to choosing a chat surface.
Compare the options on setup friction, model management, conversation surface, automation, sharing, and control. Desktop tools tend to emphasize visual discovery and management; a CLI emphasizes direct terminal interaction; APIs and SDKs emphasize integration; browser platforms can add access and collaboration features. The choice is about workflow as much as model quality.
What is the difference between a runtime, a model, and an interface?
The model is the language model you want to query. The runtime is the software that loads and serves it. The interface is how you send input and receive output. One runtime may support several interfaces, and one interface may be able to connect to more than one compatible runtime.
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It also clarifies what “local” means. The model may run on your own computer, but an application around it could still use a remote service for another feature or expose a server to other devices. Check each component’s data path instead of treating the word “local” as a guarantee about every integration.
Does browser access mean the model is in the cloud?
No. A browser is only the client interface; it can display a page served by software running locally. llama.cpp’s built-in interface and a self-hosted web platform such as Open WebUI illustrate browser access to a local model server.
What matters is where the server runs and how the browser connects to it. A page opened on the same computer is a different deployment from a service reachable by other machines. If access beyond one computer is needed, check the runtime and platform documentation for network binding, authentication, and permission settings before exposing the service.
What should I check before connecting an app to a local model?
First identify the runtime and the connection method it documents. Then confirm the client supports that method and that the model service is running where the client can reach it. An API-compatible label is helpful, but it is not proof that every operation or feature supported by one product is available in another.
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- Connection type: determine whether the integration expects a CLI, SDK, HTTP endpoint, or a provider-specific extension.
- Runtime status: make sure the model-serving process is available before asking a client to connect.
- Endpoint reachability: distinguish same-computer access from access across a network.
- Feature expectations: verify support for the particular inputs or tools your application needs.
- Data boundaries: inspect whether the client sends requests only to the local runtime or also invokes remote services.
- Access control: understand who can reach a web interface or API if it is not limited to one user on one device.
If an integration cannot connect, check those items in order before changing models. A mismatch between a client’s expected protocol and the runtime’s endpoint is a more basic issue than model selection. Use the documentation for the specific runtime and client for exact settings; the available integrations do not share one universal setup procedure.
Can one local setup support several ways to chat?
Yes, if the runtime exposes the needed interfaces and each client is compatible. A setup may provide a desktop conversation, terminal access, or a local server endpoint, with an IDE, script, or browser platform connecting through the appropriate method.
That flexibility is the central advantage of separating the model from the interface. You can use a simple surface for ordinary conversation and a more programmable one for a particular task, without assuming that every interface has the same controls or access. Confirm the capabilities of your chosen runtime and integration before planning a workflow around them.
Frequently Asked Questions
Can I use a local model without an internet connection?
A local runtime can run a model on your computer, but whether the full workflow works offline depends on what has already been installed or downloaded and whether the interface uses any online services. Check the specific runtime and client behavior rather than assuming every feature remains available without a connection.
Will a local chat remember my previous conversations?
Conversation history is a feature of the interface or application, not an automatic property of a model. Check the selected app’s history and storage settings to learn whether chats are saved and where they are kept.
Do I need a separate copy of the model for every interface?
Not necessarily. If multiple compatible clients connect to the same running model service, they can use that service rather than each launching a separate model instance. The runtime’s supported connection pattern determines whether that is possible.
Can I switch from a desktop chat to an IDE and keep the same conversation?
Changing clients does not necessarily transfer chat history: that depends on whether the applications support a shared conversation store or an import/export path. A shared model endpoint alone makes the model reachable; it does not automatically synchronize chats.
