Why Run Large Language Models Locally?

Running large language models (LLMs) locally on your computer offers several compelling advantages for developers, such as control, cost-efficiency, and a deeper understanding of how these models work. Unlike relying on online services like OpenAI’s ChatGPT, local deployment allows you to leverage your existing hardware, maintain complete control over your data, and demystify the inner workings of how these things work.

One of the primary benefits of running LLMs locally is the complete control it provides over your data, which, in turn, ensures your privacy and security. By keeping all your information on your local machine, you can rest assured that your sensitive projects are protected and are not being used by companies to train their own models. This control extends to the ability to work offline, making it ideal for scenarios where a constant internet connection isn’t possible, like a long flight or staying somewhere that has limited bandwidth. This offline capability ensures continuous accessibility without the chain that comes with always being online.

Utilizing your existing hardware to run LLMs also offers significant cost-efficiency. By leveraging your computing power, you can avoid the recurring subscription fees associated with cloud-based services. This is especially beneficial for long-term projects or continuous development, making it a more economical choice in the long run. The ability to use and optimize your hardware ensures that you get the most out of your investment. As you’ll see later one, I’ve been cobbling together computers from parts or older computers to build dedicated machines to run my LLMs on my own local network and to access remotely when I’m not at home.

Furthermore, running LLMs locally provides a valuable learning opportunity. It offers an opportunity to demystify how these models work, offering a deeper understanding of their functionality and capabilities. This hands-on experience enables fine-tuning and configuration adjustments to better suit your specific needs and be customized around your own hardware’s capabilities, leading to improved performance and more tailored results. For developers, this can significantly speed up your development, as locally running LLMs can generate code, debug, and offer real-time suggestions without the dependency on external servers. You can also use them to help document code and create tutorials to teach others.

When leveraged correctly, these models can help you scale your output and improve the quality of your code and documentation, opening up exciting opportunities for growth and improvement. There isn’t a day I don’t code without having an LLM by my side in one form or another. There is no better coding partner!