Fully Adaptive Routing Ethernet in Scale-Up Networks

The Mixture of Experts (MoE) has become a dominant paradigm in transformer-based artificial intelligence (AI) large language models (LLMs). It is widely adopted in both distributed training and distributed inference. To enable efficient expert parallelization and even tensor parallelization across dozens or even hundreds of Graphics Processing Units (GPUs) in MoE architectures, an ultra-high- throughput, ultra-low-latency AI scale-up network (SUN) is critical. This document describes how to extend the Weighted Equal-Cost Multi- Path (WECMP) load-balancing mechanism, referred to as Fully Adaptive Routing Ethernet (FARE), which was originally designed for scale-out networks, to scale-up networks.