This technical report presents a performance evaluation of several hardware model-checking tools on a collection of benchmark tasks in the Btor2 format.The resulting dataset is intended to support machine-learning research for hardware model checking, particularly in areas such as algorithm selection,performance prediction, and automated tool configuration. It has been used, for example, in the development and evaluation of Btor2-Select, a machine-learning-based algorithm selection framework for hardware model checking. To construct the dataset, we benchmarked a diverse set of model-checking tools and algorithmic configurations. Each verification engine was evaluated on a common set of Btor2 tasks and the performance measurements, including CPU time, wall time, and memory usage, were collected. All data, including scripts and files required to reproduce the experiments, are publicly available at: https://gitlab.com/sosy-lab/research/data/perf-eval-hwmc/-/tree/1.0.