Running AI models locally is mainstream now, and there are three main options most people land on. They have meaningfully different strengths. Here is an honest comparison.
Why run locally at all?
- Privacy: your data never leaves your machine
- No API costs
- Offline capability
- Ability to fine-tune and experiment freely
- Some models are only available locally
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Ollama
The simplest way to pull and run models from the command line.
Pros:
- One-line model downloads (
ollama pull llama3)
- Exposes an OpenAI-compatible API endpoint (easy to integrate)
- Lightweight, no heavy UI
- Works great in scripts and agent pipelines
Cons:
- No built-in UI (use Open WebUI alongside it)
- Less beginner-friendly without the CLI comfort
Best for: Developers, agent pipelines, anyone comfortable with the terminal.
—
LM Studio
A polished desktop app for running and chatting with local models.
Pros:
- Beautiful UI with built-in chat interface
- Easy model discovery and download
- Good model management (see what is loaded, memory usage)
- Also exposes a local server with OpenAI compatibility
Cons:
- Heavier application
- Slower to update with newest models sometimes
- Less flexible for scripting
Best for: Non-technical users, anyone who wants a nice UI, or when you want to demo local AI to someone.
—
Jan
Open-source desktop app somewhere between Ollama and LM Studio in philosophy.
Pros:
- Clean UI
- Extension system
- Fully open source
- Good for non-technical users
Cons:
- Smaller community than the other two
- Extension ecosystem still growing
- Occasional stability issues
Best for: Users who want an LM Studio alternative with a more open-source philosophy.
—
My recommendation
Use Ollama as your backend in almost all cases. Layer LM Studio or Open WebUI on top if you want a visual interface. Jan is a fine choice if open-source philosophy matters to you.
What are you running locally right now?
Curated by Selendia AI 💻
