Recent advancements in Probabilistic Circuits (PCs) and the need for exact inference in real world applications have prompted a re-evaluation of the uniform precision paradigm traditionally employed in their design for energy efficiency in hardware implementations. Hence, in this study, we propose the first mixed-precision framework dedicated to PCs, compatible with the latest hardware accelerators designed for these models. After pointing out why traditional mixed precision and pruning strategies for neural networks cannot be employed on PCs and recognizing the varying precision requirements of individual PC nodes, we explore the potential of adaptive precision models in PCs, aiming to optimize resource allocation by aligning node precision with its functional significance and the application’s tolerance for error. We verify our methodology with dedicated FPGA hardware and show that the proposed method can reduce the hardware requirements from 38 to 54% without accuracy loss, depending on the benchmark. This attests that mixed precision is efficient for sparse and irregular computations.