Agent-based modeling is a powerful tool for simulating complex systems. Some models require large amounts of agents and data. Most agent simulation platforms run models sequentially and cannot run large models in a reasonable time, or at all. To solve these problems, Agent-Based Models can use distributed computing to spread the load and/or the data over multiple computing processors. The distribution of ABM execution, however, raises complex issues that require advanced skills to be addressed. This paper presents the concept of Distribution Model that aims at simplifying the distribution of ABMs. Following the separation-of-concerns approach, we propose a flexible framework enabling a clear division between the model thematic and its distributional aspects, fostering greater flexibility in design and implementation. We present a model-based agent distribution system that uses agents to address distribution challenges. We demonstrate its capabilities using the GAMA platform, highlighting how it simplifies model distribution for researchers.