We study machine translation of Old Assyrian Akkadian into English. Hundreds of thousands of cuneiform tablets survive, most untranslated, while the largest aligned corpus for this dialect holds roughly 6,700 sentence pairs and no pretrained Akkadian language models exist. To find which choices matter in this setting, we compare the main ones a system builder faces. We test a byte-level (ByT5) against a subword (mT5) pretrained model family, model sizes from 300 million to 1.2 billion parameters, six preprocessing schemes drawn from Assyriological practice, and the quantity of training data, each measured with three random seeds. Two factors have reliable effects. Training data adds 17.4 points on our combined metric (the geometric mean of BLEU and chrF++) from a quarter of the corpus to all of it, and model size adds 7.3 points from 300 million to 580 million parameters, with one to two more at 1.2 billion. The remaining choices have smaller effects than a single shared fine-tuning recipe suggests. With all models tuned on one narrow learning-rate grid calibrated on the byte-level family, that family led by 17 to 21 points and every preprocessing scheme hurt the larger model. Choosing each configuration’s learning rate on validation data instead cuts that lead below two points and removes part of the apparent degradation. We recommend prioritising data quantity, then model size, and treating comparisons of model families or preprocessing as reliable only when every configuration is tuned separately.