How to Run a Chatbot on Your Computer Locally

I’m not exaggerating: Large language models (LLMs) behind AI tools such as ChatGPT and Google Gemini are transforming how we work, search the web, write, summarize information, and create software. Whatever the future of artificial intelligence holds, LLMs are likely to remain a central part of it.

When you use ChatGPT, Claude, Perplexity, or another AI platform, your prompts are processed by a large language model. What many people don’t realize is that you can also run an LLM locally on your own computer. The biggest advantages of a local AI model are offline access and improved privacy, since your data doesn’t need to be uploaded to a cloud service for processing or review.

Running an AI model locally can also help you avoid monthly subscriptions and usage-based fees. Many LLMs are available as free downloads from major technology companies, including Meta and Google. These models may not be as fast or capable as the latest models in paid AI apps, but they can handle everyday tasks and can be chosen according to your hardware, privacy needs, and preferred features.

The trade-off is that a local LLM requires more setup and maintenance than a cloud-based chatbot. You’ll need to install the software, manage model files, and handle updates yourself. Even so, getting started isn’t difficult, and the result is a more private, personalized AI assistant that runs directly on your computer.

What You Need to Run a Local LLM

Local LLMs can run on Windows, macOS, and Linux, although macOS is often the preferred platform among AI enthusiasts. Apple’s tightly integrated hardware and software make setup more consistent, while Apple silicon combines the CPU, GPU, and unified memory that many AI models benefit from.

Regardless of your operating system, having enough RAM is essential for running an LLM locally. A computer with 8 GB of RAM can run smaller models, but you’ll be limited in model size and performance. 16 GB is a more practical starting point, while 32 GB or more is recommended for larger and faster models. On Windows and Linux PCs, a dedicated GPU with sufficient VRAM can significantly improve performance; more than 8 GB of VRAM is especially useful because it is optimized for the parallel workloads common in AI applications.