Watch: Meta’s engineers on building network infrastructure for AI

What this post added

This post details Meta's network infrastructure evolution to support AI workloads, including GenAI training and inference. It highlights the transition to GPU-based training, the deployment of RoCE-based network fabrics with CLOS topologies, scaling RoCE networks with RoCEV2 transport, and the implementation of centralized traffic engineering for AI training clusters. It also introduces network observability tools like ROCET and PARAM benchmarks within the Chakra ecosystem, and the Arcadia simulator for end-to-end AI system performance simulation.