This article is a good step toward understanding how things really work. However, quite a few factors related to the experimental setup need to be taken into account if you want to draw more reliable conclusions: 1. The optimal shape of each architecture may differ under compute-optimal settings. 2. The optimal initialization of each parameter may differ across architectures. 3. Different architectures may respond quite differently to different optimizers. 4. Differences in training stability and the health of “internal metrics” can lead to drastically different results, even for the same architecture. 5. Architectures may respond differently to varying degrees of overtraining (tpp). 6. Different architectures may respond quite differently to different data distributions (i.e., different learning objectives). 7. The difficulty of scaling up can vary considerably across architectures. There are many more nuances that need to be carefully addressed to obtain consistent results.