Ray tracing is a powerful technique to generate realistic images of 3D scenes. However, rendering complex scenes may easily exceed the processing and mem-ory capabilities of a single workstation. Distributed processing offers a solution if the algorithm can be parallelised in an efficient way. In this paper a hybrid scheduling approach is presented that combines demand driven and data paral-lel techniques. Which tasks to process demand driven and which data parallel, is decided by the data intensity of the task and the amount of data locality (co-herence) that will be present in the task. By combining demand driven and data driven tasks, a better load balance may be achieved, while at the same time the communication is spread evenly across the network. This leads to a scalable and efficient parallel implementation of the ray tracing algorithm with little restric-tion on the size of the model data base to be rendered.