Amazon describes EKS + EFA + DeepEP architecture to scale MoE reinforcement learning with up to 40% higher throughput
An Amazon post details an architecture that combines Amazon EKS, Elastic Fabric Adapter (EFA), and DeepEP to address the heterogeneous compute, high-bandwidth communication, and orchestration challenges of post-training MoE reinforcement learning (including RLHF and GRPO). The write-up explains balancing rollout generation and tightly coupled policy training, optimizing Expert Parallelism (EP) communication over EFA, and reports up to a 40% throughput improvement using DeepEP.