LM Studio vs Ollama: which should you use?

What you’ll learn

Whether to run local models through LM Studio or Ollama, what genuinely separates them, and when it makes sense to use both. Both are inference engines with a friendly layer on top, so this is a question of workflow more than capability.

The short answer

If you would rather not touch a command line, start with LM Studio: it is a graphical app you can install and click through. If you want a model running behind an API that other tools can call, or you are comfortable in a terminal, start with Ollama. Neither is better than the other. They are built for different first steps, and plenty of people end up running both.

What each one is

LM Studio is a desktop application. It gives you a visual browser for finding and downloading models, picks sensible quantisations and shows whether a model will fit your hardware, and puts a chat window and a built-in local server behind a graphical interface. It is closed source, and free to use (see the licence section below).

Ollama is a command-line tool that wraps llama.cpp. One command pulls and runs a model, and it exposes a local, OpenAI-compatible API on port 11434 that a large ecosystem of editors, chat front-ends, and coding tools already speaks to. It is open source under the MIT licence.

At a glance

	Ollama	LM Studio
Interface	Command line and API	Graphical desktop app
Suits	Developers, tool integration, terminal users	People who prefer a GUI, no command line
Finding models	Pull by name or tag	Visual browser with fit and quantisation hints
Local server	OpenAI-compatible, on port 11434	OpenAI-compatible, built in
Apple Silicon	Uses an MLX backend	Offers MLX builds of many models
Licence	MIT, open source	Proprietary, free for personal and business use
Scripting	Strong, it is a CLI	Limited
Platforms	macOS, Windows, Linux, Docker	macOS, Windows, Linux

The differences that matter

Interface and workflow

This is the real fork in the road. LM Studio is for people who want to see models in a window, click to download, and chat in an app, with nothing to type at a prompt. Ollama is for people who are happy running ollama run <model> and who want the model available as a service for other software. If you are not sure which you are, that is a strong sign to start with LM Studio.

Models and quantisation

Both let you run the same open models; the difference is how you choose one. LM Studio’s browser surfaces the available quantisations and flags whether a build is likely to fit your memory, which takes a lot of the guesswork out of a first download. Ollama hides that behind a default per model, which is simpler but gives you less to decide, and pulling a specific non-default quantisation takes an extra step. Our quantisation guide covers what those formats mean.

Apple Silicon

Both run well on Apple Silicon and make use of the GPU through Apple’s MLX framework: Ollama uses an MLX backend, and LM Studio offers MLX builds of many models in its browser. On a Mac, either is a good choice.

The local server and the ecosystem

Each exposes an OpenAI-compatible local server, so other tools can talk to a model running on your machine as if it were a hosted API. Ollama’s has been around longer and is the more widely integrated: a great many coding tools and chat front-ends target it by default. LM Studio’s server does the same job and is easy to switch on from the app. If your reason for running a model locally is to point an editor or an agent at it, Ollama’s ecotype is the safer default; if it is to chat and experiment, LM Studio is more comfortable.

The licence, which is the one to read

Ollama is MIT, so it is open source and unrestricted. LM Studio is closed source but free to use: since 8 July 2025 it has been free for personal and internal business use, with no separate licence to request or form to fill in (LM Studio, July 2025). The conditions worth knowing: you may not redistribute, resell, offer it as a hosted or software-as-a-service product, or build it into a product you sell, without an Enterprise agreement, and there is an optional paid Enterprise tier that adds things like single sign-on and model access controls. For nearly everyone running models on their own or their team’s machines, both are free; the difference only bites if you plan to redistribute or embed LM Studio. Read the current terms before you rely on this, as licences change; we are describing them, not giving legal advice.

Can you use both?

Yes, and it is a common setup. Use LM Studio to browse, download, and try models in a comfortable interface, and use Ollama to serve a chosen model to your other tools. Because both speak the same OpenAI-compatible protocol, nothing you learn in one is wasted in the other.

Which should you use?

• Start with LM Studio if you want a graphical app, prefer not to use a command line, and mainly want to chat with and explore models.
• Start with Ollama if you are comfortable in a terminal, want a local API for coding tools or agents, or prefer an open-source stack.
• New to all of this? Either is a fine first step. Choose by whether you would rather click or type; you can always add the other later.

Next steps

• New here: choose your first model, then come back and run it.
• Want the fuller picture of what sits underneath these apps: inference engines explained.
• Tuning for performance or control: llama.cpp gives you the most direct grip on how a model runs.