RACK-SCALE NETWORKS ARE THE NEW HOTNESS FOR MASSIVE AI TRAINING AND INFERENCE WORKLOADS

Summary from the TLDR newsletter

Rack-scale architectures use interconnect technologies to offer several orders of magnitude higher bandwidth per accelerator. The bandwidth means that GPU compute and memory can be pooled, even when physically distributed across multiple distinct servers. The transition to rack-scale architectures is being driven by the demands of model builders. They are primarily aimed at hyperscale cloud providers, neo-cloud operators, and large enterprises that need to keep their AI workloads on-prem.