Running open models locally: LM Studio vs Ollama vs Jan

Deciding to run AI models on your own machine takes about five minutes. Choosing how to run them takes a weekend you will not get back.

Every Reddit thread and YouTube tutorial points at the same three tools: Ollama, LM Studio, and Jan. They all run the same open-weights models. They all keep your data on your machine. And they serve genuinely different users.

The short version: raw inference speed is within a few percent across all three — they all build on the llama.cpp engine — so the real differentiators are setup friction, interface philosophy, and how each tool fits into the rest of your workflow. Pick by workflow, not by benchmark.

Ollama: the quiet infrastructure#

Ollama is the only one of the three that does not really want to be an app. Install it with a single command or installer, and it becomes a background service on your machine that exposes an OpenAI-compatible API at localhost:11434. You pull a model with ollama pull llama3.1 or ollama pull qwen2.5:7b, and from then on, it just sits there — ready for any tool that speaks the OpenAI protocol to use.

That always-on, headless design is Ollama's superpower. Open WebUI, Continue.dev, Cursor's local mode, Aider, and dozens of other tools point at Ollama's API and work immediately, with no adapter code and no configuration. If you want to call a local model from a Python script, a shell pipeline, or your editor, Ollama is the backend that makes local AI feel like plumbing rather than a program you have to open.

Its Modelfile system is another genuine differentiator: you can package a model with its system prompt, temperature, and context-length settings into a named, first-class variant — roughly what a Dockerfile is to containers. Neither LM Studio nor Jan has an equivalent.

The trade-offs are real but straightforward. There is no native GUI — you run everything from the terminal, and you need a separate frontend (most commonly Open WebUI) for a chat experience. The model registry is curated rather than exhaustive: it covers the major models and gets new ones within days of release, but obscure Hugging Face quantizations may require a manual Modelfile. Idle RAM overhead is minimal — roughly 50 MB — which matters when every gigabyte is going to the model itself.

Ollama is free and open source (MIT license), runs on macOS, Windows, and Linux, and is Docker-friendly. It is also the only one of the three that naturally fits a headless server, a Mac Mini tucked behind a desk, or a Linux VPS.

Best for: developers, scripting, coding-tool integration, headless servers, and anyone who wants local AI to be invisible infrastructure.

LM Studio: the model browser#

LM Studio is what happens when someone with design taste builds a GUI for local AI. Download the desktop installer for macOS, Windows, or Linux, open the app, and you are looking at what is widely regarded as the best model browser in the business: thousands of models pulled from Hugging Face, filterable by size, format, and quantization, with a live compatibility indicator that tells you before you download whether a model will fit in your hardware's memory.

Finding, downloading, and chatting with a new model takes about 90 seconds, start to finish. That speed of experimentation is LM Studio's whole argument for existing. The chat interface is polished, side-by-side model comparison lets you benchmark two models on the same prompt visually, and the granular GPU-offload controls let power users tune exactly how a model is split between GPU and CPU. On Apple Silicon, LM Studio also benefits from MLX-based builds, substantially faster than llama.cpp on M-series chips.

LM Studio exposes a local OpenAI-compatible server on demand (default port 1234), so your tools can talk to it, but always-on serving is not its default mode. It is a single-app GUI first and foremost.

The one real caveat: LM Studio is closed-source. It is free for personal use, and that fact alone is a dealbreaker for a meaningful slice of the local-AI community — the people who go local precisely for transparency and control. That objection is fair and worth taking seriously.

Best for: experimenting with lots of models, prompt testing, hardware tuning, and users who want zero terminal commands.

Jan: the open-source all-rounder#

Jan sits in the interesting middle: a polished desktop GUI like LM Studio, but fully open source like Ollama. Built by the team at Homebrew (Menlo Research) and with over 43,000 GitHub stars, it is one of the most popular open-source local AI apps in existence. Its license has shifted over time — earlier releases were AGPL-3.0, and the current GitHub repository carries an Apache 2.0 license — but either way, the source is fully auditable, which matters if your reasons for going local include privacy posture or regulatory requirements.

Jan's unique selling point is that it is not local-only. In the same clean, ChatGPT-style interface, you can run a local model and connect to cloud APIs — OpenAI, Anthropic, Groq, Mistral — so you can move fluidly between a private local model for sensitive material and a frontier cloud model for the hard questions without switching apps. None of the other two do that.

Under the hood, Jan runs llama.cpp (earlier builds used its own Cortex engine), supports GGUF quantized models from Hugging Face, stores everything — models, conversations, settings — in a local folder you own, and runs zero telemetry by design. It exposes an OpenAI-compatible local server on port 1337, supports MCP (Model Context Protocol) servers for agentic tool use, offers custom assistants with saved system prompts, and ships a plugin/extension system. There is even a Docker image for headless deployment.

Where Jan lags is ecosystem depth. The community is smaller than Ollama's, so new-model support arrives more slowly, the model browser is less rich than LM Studio's, and there are fewer tutorials and plugins. Reviewers consistently describe it as "getting better every release" — true, but the bar set by LM Studio's UI and Ollama's ecosystem is high.

Best for: privacy-first users who need auditability, teams with open-source licensing requirements, and anyone who wants one unified app for local and cloud models.

Head to head#

A note on speed: independent comparisons consistently find raw token-generation throughput within about 5% across all three for the same model and quantization, because they share the same llama.cpp foundation. LM Studio has an edge on Apple Silicon via MLX builds, but for the mainstream experience, speed is not a meaningful decision factor. Ask "which fits my life," not "which is faster."

Setup friction, in practice#

• Ollama: one command, a pull, and you have a scriptable backend in under a minute. Add Open WebUI (a Docker container away) if you want chat. Total path to a private ChatGPT-feeling setup: about ten minutes, most of it model download time.
• LM Studio: install the app, click Discover, search, download, chat. No terminal at any point. The single easiest first run of the three.
• Jan: install the app, pick a model from the hub, chat — plus an optional step of adding your cloud API keys for the hybrid mode. Slightly more setup than LM Studio, but one app covers both worlds.

The takeaway#

For 95% of people, the honest recommendation is a combination rather than a single winner: Ollama as the always-on backend, LM Studio as the model-shopping GUI, and Open WebUI as the daily chat interface. The three coexist fine (Ollama defaults to port 11434, LM Studio to 1234), though each loads its own copy of a model into memory.

But if you want exactly one answer, use this decision tree:

• "I just want the ChatGPT feeling, privately" — Ollama + Open WebUI.
• "I want to try 20 models this weekend" — LM Studio.
• "I'm a developer integrating local AI into a script" — Ollama.
• "Open source is non-negotiable" — Jan, or Ollama + Open WebUI.
• "I use Claude too, and want local for private stuff" — Jan.
• "I'll run this on a headless Mac Mini" — Ollama, not even close.

All three are free, and install times are measured in minutes. The only way to find the one that fits your brain is to actually use one — and the cost of picking wrong is roughly the length of one model download.