"Moore's Law is no longer driving exponential increases in performance, pushing AI workloads to require high-bandwidth memory and parallel compute, now at an exorbitant cost."
"As the demand for AI capabilities grows, the implications of scaling up networks and the necessity for larger memory domains present new challenges for datacenter economics."
Moore's Law has effectively plateaued, forcing AI workloads to rely on parallel computing and high-bandwidth memory positioned closely to processors, both contributing to rising costs. With systems like Nvidia’s DGX server nodes, computing and networking expenses are increasingly intertwined. As AI models grow in complexity, the requirement for larger memory domains and robust interconnects becomes essential, influencing datacenter design and costs. These expenses include die-to-die and chip-to-chip interconnects within GPU systems, attributed largely to substantial software development efforts necessary to sustain these advanced functionalities.
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