LM Studio vs Ollama: which should you use?

Before the differences, it helps to see how much overlaps. Both are Model runners, and both do the same core job:

• They load and serve a local model. Each downloads a model and runs inference on hardware you own, so there is no token bill from a provider.
• They can expose an OpenAI-compatible local endpoint. Other tools, an Interface, or an Agent runner can connect to that local API much like they would connect to a hosted API.
• They do not decide whether a model fits. Fit is set by memory and model size, not by which runner you install. Switching runners does not make a model that is too large fit.

LM Studio is a desktop GUI app. It gives you a visual model browser to find and download models, a chat window to talk to them, and a local server you can switch on when another app needs to call the model. If you want to try local models by clicking around in an app rather than typing commands, LM Studio tends to be the easier place to start.

Ollama is a lightweight command-line tool and background service. You pull a model with a command, and it runs as a service that exposes a local API for other tools to call. If you want a local model that scripts, apps, or an Agent runner can reach, or you want it running quietly in the background, Ollama tends to fit that need more naturally.

The clearest way to choose is to name what you are actually doing:

• Personal local chat in a visual app. LM Studio's desktop interface and model browser are built for this.
• A local model other software calls. Ollama's command-line workflow and background service are built for this, though LM Studio's local server can also do it.
• An interface on top of a runner. A separate Interface such as Open WebUI can sit above either one, connecting to the local endpoint instead of replacing the runner.

Whichever runner you pick, the same limit applies: a model has to fit in memory to run well, and a model that fits can still be slow. That depends on RAM, VRAM or unified memory, quantization, and context length, not on the runner. See can my computer run a local LLM for how to check fit, and why local AI is not always free for the costs that stay even without a token bill. If you are weighing local against a hosted API, see local LLM vs cloud API.

You do not have to pick one forever. Many people use LM Studio to browse and try models visually, then run Ollama as the background service that apps and agents connect to. They are Model runners doing the same job in different styles, so keeping both installed is a reasonable way to cover both the visual and the service side.

• Expecting the runner to change what fits. Switching from one Model runner to the other does not add memory. A model that is too large stays too large.
• Confusing a Model runner with an Agent runner. These tools serve text from a model. Managing steps, tools, retries, and state is the job of an Agent runner, not of LM Studio or Ollama.
• Assuming local means free. There is no token bill, but you still pay in hardware, power, and setup time getting the runner working and the model to fit.

AIStackPicker treats LM Studio and Ollama as two Model runners in the local path, not as a model choice. The Builder keeps the runner separate from the model and the Local hardware, so you can see that picking a runner is about how you want to run and connect a local model, while whether the model fits is decided by memory. It does not rank one runner as universally better. It points each need at the runner that fits it.

• Cost to run an LLM
• Cost to run an AI agent
• Local LLM vs cloud API
• Run a local LLM
• GPU hosting vs model API
• Browse all stack parts
• Read the FAQ