Title: Sweet Gradient Matters: Designing Consistent and Efficient Estimator for Zero-Shot Neural Architecture Search. Abstract: Neural architecture search (NAS) is one of the core technologies of AutoML for designing high-performance networks. Recently, Zero-Shot NAS has gained growing interest due to its training-free property and super-fast search speed. However, existing Zero-Shot estimators commonly suffer from low consistency, which limits the reliability and applicability. In this paper, we observe that Sweet Gradient of parameters, i.e., the absolute gradient values within a certain interval, brings higher consistency in network performance compared to the overall number of parameters. We further demonstrate a positive correlation between the network depth and the parameter ratio of sweet gradients in each layer. Based on the analysis, we propose a training-free method to find the Sweet Gradient interval and obtain an estimator, named Sweetimator. Experiments show that Sweetimator has superior consistency to existing Zero-Shot estimators on four benchmarks with eight search spaces. Moreover, Sweetimator achieves state-of-the-art performance on NAS-Bench-201 and DARTS search spaces.