Stochastic detection for multi-antenna (MIMO) systems promises communications performance close to max-log detection for certain SNR regimes, especially when the system iterates between detector and channel decoder following the Turbo Principle. In this work, we propose a parallel VLSI architecture for soft-input soft-output Markov chain Monte Carlo based stochastic MIMO detection. It features run-time adaptability to varying channel conditions, effectively allowing us to adjust the invested effort. Besides the details of our area-throughput efficient design, like the low-level algorithm and micro-architecture design, we also provide an extensive data set from our experiments regarding the detector’s communications performance and relate it to our VLSI implementation results. The provided data analysis highlights the architecture’s run-time adaptability and demonstrates how we can trade off throughput for improved communications performance.