6 Easy Ways to Run LLM Locally + Alpha

1. LM Studio

LM Studio is a powerful desktop application designed for running and managing large language models locally. It offers a user-friendly interface for downloading, running, and chatting with various open-source LLMs. New in LM Studio 0.3.5: headless mode, on-demand model loading, and MLX Pixtral support! These features enhance flexibility and performance, allowing users to run models without a graphical interface, load models as needed to save resources, and utilize the latest advancements in AI technology

New in LM Studio 0.3.5: headless mode, on-demand model loading and MLX Pixtral support!

2. GPT4ALL ⭐70.6K

GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.

No API calls or GPUs required.

GPT4All is an ecosystem of open-source chatbots and language models that can run locally on consumer-grade hardware. It offers a unique approach to AI accessibility, allowing users to run sophisticated language models without the need for powerful GPUs or cloud services. GPT4All includes a user-friendly chat client, a Python API for developers, and supports various pre-trained models optimized for different tasks and languages.

3. Ollama ⭐97.3K

Ollama is a powerful tool for running large language models locally. It simplifies the process of downloading, installing, and running various LLMs on personal computers. Ollama has gained popularity due to its ease of use and extensive model library. It offers Python and JavaScript libraries, making it an excellent choice for developers looking to integrate LLMs into their applications:

Ollama also supports custom model creation and fine-tuning, allowing users to tailor models to specific use cases.

4. Jan ⭐23.3K

Jan is an open-source alternative to ChatGPT that runs entirely offline on your desktop. It aims to make AI accessible to everyone while prioritizing user privacy and control. Jan features a built-in model library, supports connecting to remote AI APIs, and offers a local API server with OpenAI-equivalent functionality. It also includes an extension system for customization and supports a wide range of popular LLMs like Llama, Gemma, and Mistral.

5. llamafile ⭐20.4K

support OS: Windows, Linux, MacOS

llama.cpp is written in C++ and is the fastest implementation of LLaMA and it is used in other local ans web-based applications.

But it is not easy as well as the above applications to install so that is a reason why this is an optional way to run LLM locally.