Taylor Ye

When building AI capabilities, many teams fall into a common trap: devoting too much attention to the question, "How many GPUs do we actually need?" In enterprise AI infrastructure planning, however, the real challenge is not simply the number of accelerators. It is determining the right resource mix

AI workflows are undergoing a massive shift: moving from "can we get this model to run?" to "can we reliably deploy it to production?" Today, developers face challenges that extend far beyond model selection and inference code. Enterprise engineering teams must now piece together a sustainable

Data centers have always reflected the dominant computing paradigm of their time. For many years, enterprise software, web services, and databases shaped how infrastructure was designed and operated. These workloads emphasized reliability, steady performance, and efficient resource sharing. Artificial intelligence introduces a fundamentally different demand profile. Training and deploying modern