Ollama vs LM Studio vs Jan: The Best Way to Run Local LLMs (2026 Guide)

Why run an LLM locally at all?

Running a large language model on your own hardware means no monthly API bills, no data leaving your machine, and the ability to work fully offline. What changed over the past two years is that this actually became practical: models that once needed a data center now run comfortably on a MacBook Pro or a mid-range Windows PC with a decent GPU. Three tools have emerged as the main on-ramps, and picking between them mostly comes down to whether you want a command line, a graphical app, or a balance of both.

Ollama: the developer default

Ollama is the most popular option (its open-source project has drawn well over 180,000 GitHub stars) and it is built on the llama.cpp inference engine. It is CLI-first: you install it, run 'ollama run <model>' to pull and chat with a model, and it exposes a local REST API on port 11434 that a huge ecosystem of apps, editors, and agent frameworks can plug into. If you want to wire local AI into your own scripts, VS Code, or tools like Open WebUI, Ollama is the natural backbone. It runs on macOS, Windows, and Linux and is MIT-licensed.

LM Studio: the power-user GUI

LM Studio is a polished desktop application aimed at people who want a graphical interface without giving up control. It has a built-in model browser (pulling from Hugging Face), a chat UI, per-model configuration, and the ability to run a local server that mimics the OpenAI API, so you can point existing OpenAI-based code at it. It is the sweet spot for power users who want to experiment with many models, tune parameters, and see resource usage without living in a terminal.

Jan: the newcomer-friendly choice

Jan is an open-source desktop app designed to be the easiest starting point, especially for people new to local AI or those who want a clean, ChatGPT-like experience that is fully offline and privacy-focused. It bundles model downloads and chat into a simple interface with minimal setup. If your priority is 'just let me chat with a private model without configuring anything,' Jan is the gentlest introduction.

Which one should you pick?

The quick decision guide: choose Ollama if you are a developer or CLI user who wants to integrate local models into other tools and agents. Choose LM Studio if you want a powerful GUI to browse, run, and fine-tune many models with visible controls. Choose Jan if you are new to this and want the simplest possible private chat app. They are not mutually exclusive, many people run Ollama as the engine and use a separate chat UI on top, and all three are free.

Hardware: what you actually need

The main constraint for local LLMs is memory. On Apple Silicon, unified memory is shared with the GPU, so a 16GB Mac can run small-to-mid models and 32GB+ handles larger ones comfortably. On Windows/Linux, your GPU's VRAM matters most: 8GB VRAM runs quantized 7-8B models, while 16-24GB opens up larger 13B-34B models. Quantization (running models at reduced precision, like 4-bit) is what makes bigger models fit on consumer hardware with only a modest quality trade-off, all three tools support it out of the box.

Related on Skillo

See also: 7 open-source ChatGPT alternatives you can run locally, Graphify: codebase knowledge graphs for AI agents.

Sources

• Ollama (official GitHub repository)
• Running Local LLMs in 2026: Ollama, LM Studio, and Jan Compared (dev.to)

Published date reflects the original event date (2026-08-14). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.