#AI

Having a local LLM to help you out in your day-to-day tasks can be a game-changer. Whether you're a researcher, a developer, or a business owner, having a local LLM can save you time and money, and specially give you more control over your data and the conversations you have with your AI model. Lately, in 2025, many concerns have been raised on how the AI companies are using the data users are providing to them via the use of the chatbots. Because of this, data control, specially when it relates to private documents, is becoming more important than ever. Therefore, despite the cost of maintaining a local LLM, it is a better option for those who value their data privacy and security. This post will be a guide to help you set up a local LLM on your server and serve as documentation of the process for future references…

Self-hosted storage is almost always more cost-effective for large-scale, persistent needs, specially if there is a focus on data privacy and security. For GPU compute (Deepseek), cloud is often cheaper unless you maintain high utilization (>20–30%) so there is to be mindful of the utilization rate and what the goals are before investing. After some research, a hybrid approach—self-hosted storage, cloud GPUs for burst analysis—is optimal for most organizations but all depends on the use case and goals. See detailed cost, risk, and operational breakdowns below for informed decision-making given your own scenario…

I have been asked to design a machine capable of running Deepseek locally for a company that is interested in leveraging AI to improve their processes. The company is strict about data governance, security, and—importantly—budget…