the wireless industry anticipates that a future 6g standard supports an artificial intelligence / machine learning (ai/ml) based air interface natively. in this joint demonstration, rohde & schwarz and nvidia demonstrate a neural receiver approach, using a trained machine learning model for signal processing tasks such as channel estimation, channel equalization, and demapping. due to the lack of a 6g standard, we showcase a 5g nr pusch multi-user mimo scenario, emulating two users with 2x2 mimo, that are independently faded to simulate realistic channel conditions using the r&s smw200a vector signal generator. the signal is captured using the msr4 satellite receiver and transferred to a server. our server-based testing (sbt) framework, including vector signal explorer (vse) microservices is used to pre-process the data. the generated post-fft data is input to nvidia's sionna software framework, an open-source library for 6g physical layer research. we showcase the performance based on a bler over sinr measurement and compare it to ideal, perfect channel knowledge performance and traditional receiver implementation used in today's 4g lte and 5g nr networks.