Once organizations move beyond experimenting with a small handful of large language models (LLMs), the limits of manual model deployment become clear. What may work for early testing and development quickly turns inefficient, expensive, and difficult to scale. As the number of models, variants, and versions grow, teams are left not only managing increasing operational complexity, but also determining which GPU resources are the best fit for each workload.This challenge often turns into a kind of hardware-model Tetris. Most enterprises operate with a diverse mix of GPU infrastructure, from cutt