5G, the latest standard for mobile networks, has become increasingly common in cities but has yet to reach its full potential. While it currently offers only marginal improvements over 4G, 5G holds significant potential for further development. When devices communicate over a 5G network, data is encoded before transmission and decoded on the receiving side. Thegoalofthis project is to enhance the decoding process by leveraging the strengths of GPUs. One way to achieve this is by reducing the redundancy that occurs when data is transferred from the CPU to the GPU. In the original version, this transfer happens every time data is sent, which is inefficient. The proposed solution is to perform the transfer only when there is a change in the parity matrix, which is part of the transmitted data. The second improvement involves exploiting GPUs’ faster processing capabilities with single-precision calculations compared to double-precision calculations. In other words, computations are faster when the data type is float instead of double. The improvements are implemented in the decoder that was provided. Both of these improvements have been shown to reduce execution time and demonstrate significant potential for future optimization opportunities. The optimizations show an improvement of 18-32%. All tests are conducted on an NVIDIA GeForce RTX 3090 graphics card using simulated input data from MATLAB.