Organizing small neural networks using a project institute model

The author, a thermal engineer with 14 years of experience, shares his experience working with local neural networks. Instead of creating a single 'swarm' of models, he applied a project institute structure to them, dividing tasks into specialized sections. The experiment revealed that this approach significantly increases the efficiency of weak models, allowing them to handle workloads better than a single powerful model. The author details five key points of this architecture, noting that partitioning provides a noticeable performance boost specifically for small neural networks. This method demonstrates that proper organization of workflows within a system can be more important than the raw computing power of individual components. The article is useful for specialists looking for ways to optimize local LLMs without the need for massive computational resources.