Building Networks For AI Workloads
Search Networking, Thursday, April 11th, 2024
Conventional and high-performance computing networks cannot adequately support AI workloads, so network engineers must build specialized networks to accommodate their massive size.
The rapid rise of AI highlights the need for powerful and efficient networks dedicated to supporting AI workloads and the data used to train them.
Data centers built for AI workloads have different requirements than their conventional and even high-performance computing (HPC) counterparts. These workloads don't rely solely on legacy server components. Instead, computing and storage hardware should integrate GPUs, data processing units (DPUs) and smartNICs to accelerate AI training and workloads.
Once integrated, networks must stitch these infrastructure components together and handle workloads with different parameters and requirements. Thus, data center and cloud networks designed for AI must adhere to a unique set of conditions.