# Best Local LLM Runners in 2026

Source: https://listme.name/best/local-llm-runners

Local model runners let you run open AI models on your own computer instead of a hosted service. This ranking weighs ease of setup, the interface for everyday use, license terms and how much control developers get.

Use this list to make a shortlist. Scores are editorial judgments, not benchmarks or user ratings. Publisher-connected products receive a disclosed ranking adjustment; see https://listme.name/methodology. Confirm current price models, platforms and terms on the vendor's site.

## 1. Ollama (score 92.0)

The simplest way to run open models locally on macOS, Linux and Windows, with an MIT-licensed runtime. Cloud models and higher tiers are paid, and speed depends on your hardware.

- Best for: Running open models from a command line
- Free plan, paid upgrades | macOS, Linux, Windows | open source (MIT)
- Website: https://ollama.com/
- Page: https://listme.name/software/ollama

## 2. llama.cpp (score 90.0)

The engine many local tools build on, with GGUF support, a server and a library API. It needs command-line comfort, so beginners will find Ollama or Jan easier.

- Best for: Developers who want a local inference engine
- Free and open source | Command line | open source (MIT)
- Website: https://github.com/ggml-org/llama.cpp
- Page: https://listme.name/software/llama-cpp

## 3. LM Studio (score 89.0)

The friendliest graphical way to download and chat with local models, with a local server for other tools. The app is closed source, unlike llama.cpp or Jan, and it is aimed at desktops rather than servers.

- Best for: Trying local models through a polished desktop app
- Free | Windows, macOS, Linux
- Website: https://lmstudio.ai/
- Page: https://listme.name/software/lm-studio

## 4. GPT4All (score 87.5)

A desktop app for Windows, macOS and Linux with local document chat and an MIT license. It offers fewer controls than the llama.cpp engine it builds on.

- Best for: Desktop chat with local documents
- Free and open source | Windows, macOS, Linux | open source (MIT)
- Website: https://www.nomic.ai/gpt4all
- Page: https://listme.name/software/gpt4all

## 5. Jan (score 86.0)

An open-source desktop chat app for macOS, Windows and Linux that runs offline and can connect to cloud providers. It is a finished app rather than an engine, so it gives less developer control than llama.cpp.

- Best for: Offline chat on a personal computer
- Free and open source | macOS, Windows, Linux | open source (Apache-2.0)
- Website: https://www.jan.ai/
- Page: https://listme.name/software/jan

## 6. KoboldCpp (score 84.5)

Single-file AGPL-3.0 runner for GGUF models, with the KoboldAI Lite interface and image, speech and music generation on Windows, Linux and macOS. Its copyleft license and wider feature set place it below Ollama.

- Best for: Single-file local writing and chat with GGUF models
- Free and open source | Windows, Linux, macOS | open source (AGPL-3.0)
- Website: https://github.com/LostRuins/koboldcpp
- Page: https://listme.name/software/koboldcpp

## 7. textgen (score 82.5)

AGPL-3.0 desktop app for local models with text, vision and tool-calling support, an OpenAI- and Anthropic-compatible API and portable builds for Linux, Windows and macOS. It suits people comfortable with local setup.

- Best for: Local models with an API for apps and tools
- Free and open source | Linux, Windows, macOS | open source (AGPL-3.0)
- Website: https://github.com/oobabooga/textgen
- Page: https://listme.name/software/textgen

## 8. llamafile (score 81.0)

Packages a model and its runtime into one executable that runs on macOS, Linux, Windows and BSD. Windows cannot run executables over 4 GB, which rules out larger models there.

- Best for: Sharing a model as a single portable file
- Free and open source | Command line, macOS, Linux, Windows | open source (Apache-2.0)
- Website: https://github.com/mozilla-ai/llamafile
- Page: https://listme.name/software/llamafile

## 9. MLX LM (score 80.0)

MIT-licensed Python package for running and tuning models with MLX on Mac, installed with pip or conda. Large models need macOS 15.0 or later, and the tool is limited to Mac computers, so it ranks below the cross-platform runners.

- Best for: Python scripting of local models on a Mac
- Free and open source | macOS, Command line | open source (MIT)
- Website: https://github.com/ml-explore/mlx-lm
- Page: https://listme.name/software/mlx-lm

## 10. LocalAI (score 79.0)

A self-hosted server for text, image, voice and video models, MIT licensed and usable without a GPU. It runs as a server, so it is less direct for a personal chat app.

- Best for: Self-hosted AI server for several model types
- Free and open source | Self-hosted | open source (MIT)
- Website: https://localai.io/
- Page: https://listme.name/software/localai

## How to choose

- **Setup effort:** Desktop apps such as Jan and GPT4All need no command line; Ollama and llama.cpp reward comfort with a terminal.
- **Use case:** A chat window suits Jan or GPT4All, a server for apps suits LocalAI, and a single portable file suits llamafile.
- **Cloud options:** Ollama sells cloud models on paid plans, and Jan can connect to cloud providers; the others here run locally only.
- **License:** Most options use MIT or Apache 2.0, which allow commercial use; check each license before shipping a product on top of one.

## Criteria

- Ease of setup
- Interface for everyday chat
- Developer control and APIs
- License terms
- Cloud options

## FAQ

**What is the easiest local LLM runner?**
Ollama and Jan are the simplest starts here. Ollama uses a one-line install and a model library, and Jan is a desktop app for macOS, Windows and Linux.

**Which local LLM runners are free?**
All ten entries are free to use. Ollama's free plan runs local models, and its paid plans cover cloud models.

**Can I run a local model without a GPU?**
LocalAI states that it runs without a GPU. For the other runners, speed depends on the hardware and model size, so start with a small model on a laptop.

**Which local runners are open source?**
Ollama, llama.cpp, GPT4All, Jan, llamafile and LocalAI all publish their code under MIT or Apache 2.0 licenses. KoboldCpp and textgen use AGPL-3.0, and MLX LM is MIT licensed.