Compare the two most popular ways to run AI models locally. Ollama offers CLI simplicity and API-first design, while LM Studio provides a polished desktop experience.
Ease of Setup
🏆 OllamaOllama wins with a single curl command to install and 'ollama run' to start. LM Studio requires downloading a desktop app but has a great onboarding flow.
User Interface
🏆 LM StudioLM Studio has a polished desktop UI with built-in chat, model browsing, and configuration. Ollama is CLI-first, requiring a separate UI like Open WebUI.
API & Integration
🏆 OllamaOllama's OpenAI-compatible API and Docker support make it the go-to for developers building applications.
Model Support
🏆 OllamaOllama has a larger curated library and faster support for new models. LM Studio supports GGUF files from HuggingFace.
Performance
🤝 TieBoth use llama.cpp under the hood and deliver similar inference performance for the same models and quantization.
Platform Support
🏆 OllamaOllama supports Windows, Mac, Linux, and Docker. LM Studio covers desktop platforms but lacks Docker/server deployment.
🎯 Which Should You Choose?
Choose Ollama if you're a developer who wants API access, Docker deployment, or CLI workflows. Choose LM Studio if you want a beautiful desktop app for chatting with models. Many users run both — Ollama as the backend server and LM Studio for casual exploration.