Lack of realistic benchmarks hinders efficient design and evaluation of analysis techniques for feature models. We extract a variability model from the code base of the Linux kernel, obtaining a model larger by an order of magnitude than the largest publicly available feature model so far. We analyze properties of this model, compare it with previously available benchmarks, and emphasize the differences from published academic examples. As a result, we broaden our understanding of what a feature model is, hopefully challenging tool designers by providing an interesting benchmark, giving input to design of random model generators, and last but not least, inspiring designers of variability modeling languages.