Analyzing the composition of Internet traffic has many applications nowadays, like tracking bandwidth consuming applications, QoS-based traffic engineering and lawful interception of illegal traffic. Even though many flow-based classification methods, such as Support Vector Machines (SVM) have demonstrated their accuracy, few practical implementations of lightweight classifiers exist. We consider in this paper the design of a real-time SVM traffic classifier at hundreds of Gb/s to allow online detection of categories of applications. We also implement a high-speed flow reconstruction algorithm able to handle one million concurrent flows. The solution is based on the massive parallelism and low-level network interface access of FPGA boards. We find maximum supported bit rates up to 408 Gb/s for classification and up to 20 GB/s for flow reconstruction for the most challenging trace. Results are confirmed using a commercial Combov2 board with Analyzing the composition of Internet traffic has many applications nowadays, like tracking bandwidth consuming applications, QoS-based traffic engineering and lawful interception of illegal traffic. Even though many flow-based classification methods, such as Support Vector Machines (SVM) have demonstrated their accuracy, few practical implementations of lightweight classifiers exist. We consider in this paper the design of a real-time SVM traffic classifier at hundreds of Gb/s to allow online detection of categories of applications. We also implement a high-speed flow reconstruction algorithm able to handle one million concurrent flows. The solution is based on the massive parallelism and low-level network interface access of FPGA boards. We find maximum supported bit rates up to 408 Gb/s for classification and up to 20 GB/s for flow reconstruction for the most challenging trace. Results are confirmed using a commercial Combov2 board with a Virtex 5 FPGA