This manuscript presents a highly efficient method for assessing the quality of ultra-widefield (UWF) fundus images, which we developed for the MICCAI Ultra-Widefield Fundus Imaging for Diabetic Retinopathy (UWF4DR) Challenge 2024 task 1. We used a lightweight MobileNetV3 model at a relatively low resolution of \(254\times 200\) pixels. We augment the training data with synthetic quality degradation to simulate additional poor quality cases and use highly efficient parallelized test time augmentations. Our method achieved the highest throughput of all submitted solutions with 24.7 images per second, while ranking third in terms of area under the ROC curve (AUROC) with an AUROC of 0.9623. Our final overall ranking for task 1 was second place. Notably, we achieve the highest specificity with 97.5%. While there are many potential avenues for improvements, e.g. better test time augmentations, our ultra-efficient UWF image quality assessment solution can process an image in 40.5 ms on a CPU, potentially enabling real-time assessment at the point of capture without specialised hardware. Our code is available on GitHub .