Appendix A. Local AI Models: An Accessible Alternative
This appendix covers
- The hardware requirements for running local AI models
- Tools for simplifying local AI setup
- Setting up your own local AI chatbot in Visual Studio Code
- Popular local AI models
- Security and ethical considerations for running AI models locally
Local AI models offer more control over your data, enhanced privacy, and a greater ability to customize AI tools to your specific needs. Unlike cloud-based models, which focus on ease of use and scalability, local models can be adapted to unique requirements and run without an internet connection—ideal for industries handling confidential data, like healthcare, finance, or government. By keeping data processing local, these models make it easier to comply with strict data protection regulations and to meet industry-specific legal requirements.
Running local AI models does have its challenges, particularly in terms of hardware requirements. Where cloud models offload computational burdens to remote servers, local models need dedicated hardware to train and run efficiently. Small Language Models (SLMs) are a practical solution here. They’re designed to operate on limited hardware, allowing organizations to deploy AI locally without needing high-end infrastructure. In this appendix, we’ll guide you step-by-step through what it takes to implement local models, showing you that it’s more straightforward than you might think.