As the internet industry evolves, the need for effective encrypted traffic classification (ETC) becomes vital for network management and cybersecurity. Meanwhile, existing deep learning (DL)-based methods struggle with balancing model complexity with accuracy. A significant challenge is deploying these models on dominant network devices in a way that ensures rapid and precise traffic classification. In this paper, we propose FasterTrafficNet, a novel DL-based lightweight ETC strategy designed for deployment on low-configuration networking devices. We designed the core component of FasterTrafficNet using a PConv-based concept, which efficiently extracts spatial features from data while reducing unnecessary computations and memory access, further enhancing the model's operational efficiency. Additionally, we integrated Do-Conv in place of conventional non-dot convolution to amplify the model's performance significantly without escalating the computational overhead during inference. We conducted a comparative analysis of FasterTrafficNet against six advanced ETC methods utilizing two publicly accessible benchmark datasets. Experimental results demonstrate that FasterTrafficNet, comprising 1.46 M model parameters, provides superior classification performance relative to the other methods. Consequently, FasterTrafficNet demonstrates a lightweight approach to ETC that can be used on extensive networks of devices.