Meta building cloud business to sell excess AI capacity!

Meta is transitioning from a consumer of hardware to a provider of cloud-scale AI infrastructure, selling excess GPU capacity. This shift poses technical challenges, such as partitioning high-performance GPU fabrics without introducing latency bottlenecks or security risks. To address these challenges, Meta must implement a rigorous control plane that handles virtualization overhead, network isolation, and job preemption. This requires a sophisticated job scheduling system that can manage internal research deadlines and third-party commercial SLAs. Engineers should be aware of these technical constraints when considering Meta's cloud business.