In this paper, we evaluate the performance of various fine-tuned, transformer-based models in translating Akkadian into English. Using annotated Akkadian data, we seek to establish potential considerations when developing models for other low-resource languages, which do not yet have as robust data. The results of this study show the potency, but also cost inefficiency, of Large Language Models compared to smaller Neural Machine Translation models. Significant evidence was also found demonstrating the importance of fine-tuning machine translation models from related languages.