Ollama vs LM Studio vs Jan – Best Local LLM Tools

Running large language models on your own hardware has gone from a hobbyist skill to a one-click affair. That is thanks to a wave of local AI tools that download open models for you, handle the GPU/CPU plumbing, and give you a clean interface — no cloud account, no data leaving your machine. In 2026 three names dominate the space: Ollama, LM Studio and Jan. They all solve the same core problem but take very different approaches, and the right choice depends on whether you live in a terminal, want a polished desktop app, or care most about staying fully open source. Here is how they stack up.

1. Ollama — The developer favourite

Ollama is the easiest way to run large language models locally, wrapping model downloading, inference and management into a single streamlined CLI tool and API server. With support for thousands of models across the Llama, Mistral, Gemma, Phi and Qwen families, it works out of the box on anything from a modern laptop to a multi-GPU server.

• One-command setup — download and run any model with ollama run llama3.
• OpenAI-compatible API — a drop-in replacement for OpenAI’s API, so your existing tools and libraries work against local models unchanged.
• Huge model library — thousands of pre-configured models maintained in an official registry.
• Custom Modelfiles — define system prompts, parameters and template formats and share them.
• GPU acceleration — automatic NVIDIA CUDA detection on Linux and Windows, plus Apple Metal on macOS.
• Ecosystem integrations — every serious GUI (including LM Studio-style apps, Continue.dev and many others) can point at an Ollama backend.

Platforms: macOS, Linux and Windows. Licensed under the MIT License — fully open source. If you build tools, scripts or any kind of local LLM pipeline, Ollama is the engine underneath almost all of them. See our full Ollama listing.

2. LM Studio — The polished desktop on-ramp

LM Studio is a desktop application for discovering, downloading and running large language models with a polished graphical interface. You browse a built-in model catalogue, load a model, and start chatting in seconds — no terminal, no environment setup. It also exposes an OpenAI-compatible local server, making it a convenient backend for prototyping.

• Local model library — browse, search and download hundreds of models from Hugging Face inside the app.
• One-click inference — load a model and chat instantly, with CPU and GPU acceleration.
• OpenAI-compatible server — start a local API so existing apps can talk to local models.
• Cross-platform GUI — clean desktop interface for macOS, Windows and Linux.
• Comparison and tuning — side-by-side model comparison and simple controls for sampling parameters.
• RAG and tooling — increasingly supports retrieval and tool workflows inside the desktop app.

Platforms: macOS, Windows and Linux. NOT open source — proprietary freemium: the desktop app is free for personal use, with some features behind commercial licensing. It is the most approachable on-ramp, but if a fully open tool matters to you, Ollama or Jan below cover the same ground. See our full LM Studio listing.

3. Jan — The open-source assistant

Jan is an open-source desktop application that runs large language models locally, built with the user experience of commercial AI assistants in mind. It offers a clean chat interface, a model hub, and full offline operation — no account, no cloud, and no data leaving your machine. Jan positions itself as the free, open alternative to closed desktop AI apps, with an extensible plugin architecture.

• 100% local and private — everything runs on your hardware and works completely offline.
• Model hub — browse and download thousands of models, including GGUF formats, from inside the app.
• Polished chat UI — a modern assistant interface with streaming responses and conversation history.
• OpenAI-compatible API — expose a local API server for integrating with other applications.
• Cross-platform — native desktop apps for Windows, macOS and Linux.
• Extensible — plugin architecture and an open codebase allow customisation and community contributions.
• Multi-modal — increasingly supports vision-capable models alongside text.

Platforms: macOS, Windows and Linux. Licensed under the Apache License 2.0 — fully open source. If you want the convenience of a local assistant app without proprietary lock-in, Jan is the most direct open answer. See our full Jan listing.

Head-to-Head

• Interface: Ollama is a terminal-first engine — great if you live in a shell or build automation. LM Studio and Jan are desktop apps with point-and-click model browsing for everyone else.
• Ease of use: LM Studio is the gentlest entry point. Jan is close behind. Ollama assumes you are comfortable on the command line.
• Licensing: Ollama (MIT) and Jan (Apache 2.0) are fully open source. LM Studio is proprietary freemium — free for personal use, but not open.
• Flexibility: Ollama is the most powerful as a building block — its API and Modelfiles make it the go-to backend for other tools. LM Studio and Jan are more turnkey assistants.
• Privacy: All three run models locally, so nothing leaves your machine. The difference is transparency: you can inspect and self-host the entire stack of Ollama and Jan, but not LM Studio.
• This is not either/or: Many people run Ollama as the engine and a desktop front-end on top. You can even point LM Studio or Jan at an Ollama backend if you prefer.

Which One Should You Pick?

Choose Ollama if you are a developer or power user who wants a fast, scriptable engine with the biggest ecosystem — or if you plan to build anything on top of local models. Choose LM Studio if you want the slickest, simplest desktop experience and do not mind a proprietary (free) app. Choose Jan if you want a genuinely open-source desktop assistant with a clean interface and no proprietary lock-in anywhere in the stack.

The Bottom Line

All three are excellent local LLM tools, and for most people the honest answer is a combination: Ollama as the reliable engine underneath, with a polished front-end on top. If you must pick one, Ollama offers the best balance of openness, capability and ecosystem; Jan is the strongest fully open desktop app; and LM Studio is the friendliest on-ramp if open source is not a dealbreaker. Whichever you choose, your models, your data and your privacy stay on your machine.

Browse the rest of our free and open source toolkit in the full directory, and if you know a local AI tool we have missed, submit it so the community can find it.