Low-latency (LL) applications, such as the increasingly popular cloud gaming (CG) services, have stringent latency requirements. Recent network technologies such as L4S (Low Latency Low Loss Scalable throughput) propose to optimize the transport of LL traffic and require efficient ways to identify it. A previous work proposed a supervised machine learning model to identify CG traffic but it suffers from limited processing rate due to a pure software approach and a lack of generalization. In this paper, we propose a hybrid P4/NFV architecture, where a hardware Tofino based P4 implementation of the feature extraction functionality is deployed in the data plane and a unsupervised model is used to improve classification results. Our solution has a better processing rate while maintaining an excellent identification accuracy thanks to model adaptations to cope with P4 limitations and can be deployed at ISP level to reliably identify the CG traffic at line rate.