A vast number of CrabNet models, totaling to 173219, were trained with different hyperparameter combinations using the Ax platform's quasi-random Sobol sampling function to generate unique parameter combinations. While there can be other uses, this dataset is primarily intended as a multi-objective, multi-fidelity, high-dimensional benchmark dataset for formulation-based optimization scenarios by scaling each of the numerical parameters to the range of 0 to 1 and applying a contrived constraint that the sum of all parameters must equal one. To realistically capture noise in the benchmark dataset, simulations were repeated for each quasi-random parameter combination. To improve efficiency and reduce latency, hyperparameter sets (including repeats) were shuffled and divided into batches, then sent to a high-performance computing environment for asynchronous evaluation. Despite some results not being completed due to either timeout or preemption, this trade-off was deemed reasonable for the efficiency and completion gains.