Why Local LLMs Are Necessary

Local LLMs are necessary for privacy and freedom, as data center subscriptions risk data misuse and account lockouts. Smaller models are becoming more capable, making local AI viable for software development despite upfront hardware costs.

Currently it is not feasible for most people to run frontier, state-of-the-art models LLMs locally . Data centers are needed to run these large models. However, I remain convinced that local LLMs are necessary and are the future of using AI for software development and other tasks. If you have a subscription to an AI service that is run from a data center, you give up a lot of privacy and freedom . Your data can be used for training even if the provider says it won't be and your subscription can be canceled any time, for any reason. Having local hardware capable of running LLMs is expensive , but used equipment can make it more affordable. I have found that smaller models have become more capable over the last year, and if this trend continues, using local LLMs for software development will become even more viable. It seems foolish to me to rent a useful tool in the long-term unless absolutely necessary. Using subscriptions to companies like OpenAI and Anthropic is logical until local options become more readily available, but it makes no sense in the longer term. The risks of being locked out of your account is too great.