Accessing ChatGPT online is very simple – all you need is an internet connection and a good browser. However, by doing so, you may be compromising your privacy and data. OpenAI stores your prompt responses and other metadata to retrain the models. While this might not be a concern for some, others who are privacy-conscious may prefer to use these models locally without any external tracking.
In this post, we will discuss five ways to use large language models (LLMs) locally. Most of the software is compatible with all major operating systems and can be easily downloaded and installed for immediate use. By using LLMs on your laptop, you have the freedom to choose your own model. You just need to download the model from the HuggingFace hub and start using it. Additionally, you can grant these applications access to your project folder and generate context-aware responses.
GPT4All is a cutting-edge open-source software that enables users to download and install state-of-the-art open-source models with ease.
Simply download GPT4ALL from the website and install it on your system. Next, choose the model from the panel that suits your needs and start using it. If you have CUDA (Nvidia GPU) installed, GPT4ALL will automatically start using your GPU to generate quick responses of up to 30 tokens per second.
You can provide access to multiple folders containing important documents and code, and GPT4ALL will generate responses using Retrieval-Augmented Generation. GPT4ALL is user-friendly, fast, and popular among the AI community.
Read the blog about GPT4ALL to learn more about features and use cases: The Ultimate Open-Source Large Language Model Ecosystem.
LM Studio is a new software that offers several advantages over GPT4ALL. The user interface is excellent, and you can install any model from Hugging Face Hub with a few clicks. Additionally, it provides GPU offloading and other options that are not available in GPT4ALL. However, LM Studio is a closed source, and it doesn’t have the option to generate context-aware responses by reading project files.
LM Studio offers access to thousands of open-source LLMs, allowing you to start a local inference server that behaves like OpenAI’s API. You can modify your LLM’s response through the interactive user interface with multiple options.
Also, read Run an LLM Locally with LM Studio to learn more about LM Studio and its key features.
Ollama is a command-line interface (CLI) tool that enables speedy operation for large language models such as Llama 2, Mistral, and Gemma. If you are a hacker or developer, this CLI tool is a fantastic option. You can download and install the software and use `the llama run llama2` command to start using the LLaMA 2 model. You can find other model commands in the GitHub repository.
It also allows you to start a local HTTP server that can be integrated with other applications. For instance, you can use the Code GPT VSCode extension by providing the local server address and start using it as an AI coding assistant.
Improve your coding and data workflow with these Top 5 AI Coding Assistants.
LLaMA.cpp is a tool that offers both a CLI and a Graphical User Interface (GUI). It allows you to use any open-source LLMs locally without any hassle. This tool is highly customizable and provides fast responses to any query, as it is entirely written in pure C/C++.
LLaMA.cpp supports all types of operating systems, CPUs, and GPUs. You can also use multimodal models such as LLaVA, BakLLaVA, Obsidian, and ShareGPT4V.
Learn how to Run Mixtral 8x7b On Google Colab For Free using LLaMA.cpp and Google GPUs.
To use NVIDIA Chat with RTX, you need to download and install the Windows 11 application on your laptop. This application is compatible with laptops that have a 30 series or 40 series RTX NVIDIA graphics card with at least 8GB of RAM and 50GB of free storage space. Additionally, your laptop should have at least 16GB of RAM to run Chat with RTX smoothly.
With Chat with RTX, you can run LLaMA and Mistral models locally on your laptop. It’s a fast and efficient application that can even learn from documents you provide or YouTube videos. However, it’s important to note that Chat with RTX relies on TensorRTX-LLM, which is only supported on 30 series GPUs or newer.
If you want to take advantage of the latest LLMs while keeping your data safe and private, you can use tools like GPT4All, LM Studio, Ollama, LLaMA.cpp, or NVIDIA Chat with RTX. Each tool has its own unique strengths, whether it’s an easy-to-use interface, command-line accessibility, or support for multimodal models. With the right setup, you can have a powerful AI assistant that generates customized context-aware responses.
I suggest starting with GPT4All and LM Studio as they cover most of the basic needs. After that, you can try Ollama and LLaMA.cpp, and finally, try Chat with RTX.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
