Tremendous-resolution (SR) methods have lately been proposed to upscale the outputs of neural radiance fields (NeRF) and generate high-quality photos with enhanced inference speeds. Nonetheless, current NeRF+SR strategies enhance coaching overhead by utilizing additional enter options, loss features, and/or costly coaching procedures corresponding to information distillation. On this paper, we goal to leverage SR for effectivity features with out pricey coaching or architectural adjustments. Particularly, we construct a easy NeRF+SR pipeline that immediately combines current modules, and we suggest a light-weight augmentation method, random patch sampling, for coaching. In comparison with current NeRF+SR strategies, our pipeline mitigates the SR computing overhead and could be educated as much as 23× sooner, making it possible to run on client units such because the Apple MacBook. Experiments present our pipeline can upscale NeRF outputs by 2-4× whereas sustaining top quality, rising inference speeds by as much as 18× on an NVIDIA V100 GPU and 12.8× on an M1 Professional chip. We conclude that SR generally is a easy however efficient method for bettering the effectivity of NeRF fashions for client units.