YOLO-NAS is a cutting-edge object detection model developed by Deci, designed to advance the capabilities of YOLO-based architectures through innovations like a dual-path backbone, multi-scale feature pyramid, and attention mechanisms. This open-source model, available through Deci's SuperGradients library, utilizes quantization-aware blocks and inference time reparametrization, offering significant improvements in throughput and accuracy over existing YOLO models, especially on the NVIDIA T4 GPU. Built on the COCO, Objects365, and Roboflow 100 datasets, YOLO-NAS supports efficient object detection tasks, with its architecture comprising a backbone, neck, and head for feature extraction, enhancement, and prediction. The training process leverages state-of-the-art techniques such as exponential moving average and zero-weight decay, and the model is fine-tuned using various datasets through SuperGradients, which provides seamless integration with PyTorch Datasets and Dataloaders. Despite lacking a formal paper, YOLO-NAS is detailed in a technical blog, and its performance is demonstrated through a comprehensive tutorial that includes model instantiation, fine-tuning, and evaluation.