Resource requirements, including time, memory, and hardware, are critical characteristics of any de novo sequencing method, but these features are not always explicitly described in published papers. In general, we can distinguish between the resources required to train a model and resources required to use that trained model to carry out de novo sequencing (Table ). All of these deep learning methods rely on GPUs for model training, with training times ranging from 24 to 80 hours, depending on the size of the model, the amount of training data, and available hardware. Some methods explicitly indicate that training or inference can be carried out using only CPUs , though in practice this is likely to be slow.