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LM Studio vs Ollama

Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.

LM Studio

AI tool · Free

A desktop app for discovering, downloading and chatting with local open models, with a local server mode.

Ollama

Open source · MIT

Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud.

Our verdict

LM Studio is a desktop app with a point-and-click model browser, aimed at people who want to chat with local AI without using a terminal. Ollama is a command-line tool and local API that suits developers and technical users who want to run models from their own code or terminal. Choose based on whether you want a visual app or a programmable backend.

Pick LM Studio if you are not a developer and want a friendly app to browse, download and chat with local models.

Pick Ollama if you are comfortable with a terminal and want a local model API for your own apps or coding tools.

Key differenceLM StudioOllama
InterfaceDesktop GUI with a model browser and chat windowCommand line with a simple REST API and libraries
SetupDesktop app download for macOS, Windows and LinuxInstall via package manager; beginner-level setup, terminal use expected
Best atDiscovering, downloading and chatting with models; comparing quantised modelsBuilding apps that call a local model API; scripting from the terminal
Price and licenceFreeFree and open source under MIT licence
Hardware needsBig models need lots of RAM or VRAM; hardware hints providedModel files can be large; the README gives no specific requirements

Written with AI from both products' catalogue entries · updated 2026-10-29

Who each one is for

Choose LM Studio if you need…

  • Non-developers running local models
  • Comparing quantised models

Choose Ollama if you need…

  • Developers who want a local model API for their own apps
  • People who want to chat with open models without sending prompts to a hosted service
  • Users who want to connect a coding assistant to a local or self-chosen model
  • Students experimenting with different open models from a terminal

Side-by-side

LM StudioOllama
TypeAI assistants · Local & open modelsApp
PriceFreeFree and open source
Licence—MIT
GitHub stars—186k
Setup difficulty—Beginner
How you get it—Install with a package manager
PlatformsmacOS, Windows, LinuxmacOS, Windows, Linux, CLI, API
Works with—VS Code
Main language—Go
Last code update—4 days ago
Hands-on tested by usNot yet—
Last checked2026-10-29—

What each one is

LM Studio

A desktop app for discovering, downloading and chatting with local open models, with a local server mode. Friendly GUI for local AI. Big models need lots of RAM or VRAM to run well.

Ollama

Ollama is a tool for downloading and running open-weight AI language models locally, with a simple command line and a REST API. The README says it also offers cloud access and can connect to coding tools and assistants such as Claude Code, Codex and OpenClaw. People use it to try models privately or to build apps that call a model through a local API.

Best for

LM Studio

  • Non-developers running local models
  • Comparing quantised models

Ollama

  • Developers who want a local model API for their own apps
  • People who want to chat with open models without sending prompts to a hosted service
  • Users who want to connect a coding assistant to a local or self-chosen model
  • Students experimenting with different open models from a terminal

Strengths

LM Studio

  • Model browser and downloads
  • Chat with documents
  • Local API server
  • Hardware compatibility hints

Ollama

  • Simple install and run commands across macOS, Windows, Linux and Docker
  • Official Python and JavaScript libraries plus a documented REST API
  • Large community with many integrations listed in the README

Not ideal for

LM Studio

—

Ollama

  • People who need the most capable closed frontier models, since this focuses on open models
  • Users with limited disk space or memory, because model files can be large and need capable hardware (the README does not give specific requirements)
  • Non-technical users who want a polished desktop app with no terminal use, although the README mentions a download option for macOS and Windows

Trade-offs

LM Studio

—

Ollama

  • The README is truncated and does not state hardware requirements or model sizes
  • Some features, such as cloud access and specific integrations, depend on Ollama's own services and documentation that is only partly described here
  • Model quality and licensing depend on each individual model, not on Ollama itself

Getting started

Ollama

macOS and Linux install

curl -fsSL https://ollama.com/install.sh | sh

Windows install (PowerShell)

irm https://ollama.com/install.ps1 | iex
Step-by-step guide: how to use Ollama

Frequently asked questions

Should I use LM Studio or Ollama?

LM Studio is a desktop app with a point-and-click model browser, aimed at people who want to chat with local AI without using a terminal. Ollama is a command-line tool and local API that suits developers and technical users who want to run models from their own code or terminal. Choose based on whether you want a visual app or a programmable backend. Pick LM Studio if you are not a developer and want a friendly app to browse, download and chat with local models. Pick Ollama if you are comfortable with a terminal and want a local model API for your own apps or coding tools.

What is the difference between LM Studio and Ollama?

LM Studio: A desktop app for discovering, downloading and chatting with local open models, with a local server mode. Ollama: Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud. LM Studio suits non-developers running local models, while Ollama suits developers who want a local model api for their own apps.

Are LM Studio and Ollama free?

LM Studio: Free. Ollama: Free and open source.

What are other alternatives to LM Studio and Ollama?

Other options people compare them with include Jan, llama.cpp, MindsHub, Reasonix, magpie.

Keep comparing

Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.