The podcast examines the full cost of training a large language model from scratch, showing that expenses extend far beyond GPU compute to include data preparation, engineering, infrastructure, and ongoing experimentation. It explains how model size, dataset volume, training duration, hardware selection, and cloud-versus-on-premises deployment influence the overall budget. The piece also highlights the operational expertise needed to build, train, evaluate, and maintain a proprietary LLM reliably. Finally, it helps organizations assess whether developing a custom foundation model is justified or whether adapting an existing open-source or commercial model offers a more practical path.

https://sam-solutions.com/blog/cost ...