In a live streaming chat on a video streaming service, it is crucial to filter out toxic comments with online processing to prevent users from reading comments in real-time.However, recent toxic language detection methods rely on deep learning methods, which can not be scalable considering inference speed.Also, these methods do not consider constraints of computational resources expected depending on a deployed system (e.g., no GPU resource).This paper presents an efficient method for toxic language detection that is aware of real-world scenarios.Our proposed architecture is based on partial stacking that feeds initial results with low confidence to meta-classifier.Experimental results show that our method achieves a much faster inference speed than BERT-based models with comparable performance.