AWS Boosts MoE AI Training Throughput by 40%

Amazon Web Services has detailed an architecture combining Amazon EKS, Elastic Fabric Adapter, and DeepEP to tackle the complex infrastructure demands of post-training Mixture-of-Experts models using reinforcement learning, achieving 40% higher throughput. The system addresses three core challenges: coordinating heterogeneous compute for rollout generation and policy training, sustaining high-throughput communication across hundreds of accelerators, and dynamically balancing all subsystems. MoE models, increasingly used to scale LLMs to trillions of parameters, introduce unique training burdens as sparsity shifts bottlenecks from compute to communication. Expert Parallelism adds dynamic token routing across devices, compounding pressure from memory, networking, and orchestration when running inference and training simultaneously on shared resources.