Sources of noise such as quantization, introduce randomness into Register Transfer Level (RTL) designs of Multiple Input Multiple Output (MIMO) systems. Performance of these MIMO RTL designs is typically quantified by metrics averaged over simulations. In this paper, we introduce a formal approach to compute these metrics with high confidence. We define best, bounded and average case performance metrics as properties in a probabilistic temporal logic. We then use probabilistic model checking to verify these properties for MIMO RTL and thereby guarantee the statistical performance. If a property fails, we show a characterization of error. However, probabilistic model checking is known to encounter the problem of state space explosion. With respect to the properties of interest, we show sound and efficient reductions that significantly improve the scalability of our approach. We illustrate our approach on different non-trivial components of MIMO system designs.