The massive throughput increase in 6G wireless communication systems, due to the use of a wider spectrum and a large number of antenna elements, have driven a huge use of AI-based technologies to achieve high system performance. Prior art shows the outstanding performances of neural receivers compared to conventional ones but this comes with a high network complexity leading to a heavy computational cost. This poses a significant challenge in the deployment of these receivers on hardware-constrained devices, making optimization strategies to reduce the computational cost a primary concern. In this work, we focus on the optimization of a Neural Receiver through two main strategies: quantization and compression.