Neural Radiance Field (NeRF) is a popular method for synthesizing novel views of a scene from a set of input images. While NeRF has demonstrated state-of-the-art performance in several applications, it suffers from high computational requirements. Recent works have attempted to address these issues by including explicit volumetric information, which makes the optimization process difficult when fine-graining the voxel grids. In this paper, we propose an ensemble approach that combines the strengths of two NeRF models to achieve superior results compared to state-of-the-art architectures, with a similar number of parameters. Experimental results show that our ensemble approach is a promising strategy for performance enhancement, and beats vanilla approaches under the same parameter’s cardinality constraint.