Training large language and multimodal models from scratch comes with challenges, especially in the hardware lottery of varying cluster quality and support levels. GPU training experiences differ significantly from TPUs, highlighting the importance of competent hardware management in achieving success in model training in the wilderness of infrastructure and code diversity.