Ray tracing is a powerful technique to generate realistic images of 3D scenes. A drawback is its high demand for processing power. Multiprocessing is one way to meet this demand. However, when the models are very large, special attention must be paid to the way the algorithm is parallelised. Combining demand driven and data parallel techniques provides good opportunities to arrive at an efficient scalable algorithm. Which tasks to process demand driven and which data driven, is decided by the data intensity of the task and the amount of data locality (coher-ence) that will be present in the task. Rays with the same origin and similar di-rections, such as primary rays and light rays, exhibit much coherence. These rays are therefore traced in demand driven fashion, a bundle at a time. Non-coherent rays are traced data parallel. By combining demand driven and data driven tasks, a good load balance may be achieved, while at the same time spreading the commu-nication evenly across the network. This leads to a scalable and efficient parallel implementation of the ray tracing algorithm.