The frenetic pace of AI development is challenging data center design and operational paradigms. The twin pillars of hyperscale computing – intelligent processors and the memory that feeds them – are no exception. Hyperscale operators have invested heavily in GPU clusters and high-bandwidth memory to drive AI model training and inference. While those investments have pioneered historic performance gains, they also exposed a design rift that data center architects are racing to close. One of today’s most pressing AI compute challenges is no longer defined by accelerating processor cycles –...

Source: The New Stack