Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput
Amazon Web Services Ashvin Nihalani
Amazon EKS, EFA, and DeepEP were used to scale post-training MoE reinforcement learning workloads by orchestrating rollout generation and tightly coupled policy training across heterogeneous nodes. DeepEP v2 native EFA support delivered 40% more throughput by replacing standard all-to-all expert-parallel communication with dispatch and combine kernels over libfabric and EFA. This shifts the bottleneck from expert-parallel inter-node communication to a more balanced end-to-end pipeline, improving rollout generation capacity and keeping training workers from idling.
Why it matters
Learn how to scale Mixture-of-Experts (MoE) reinforcement learning on Amazon EKS using Elastic Fabric Adapter (EFA) and DeepEP. This post presents an architecture that combines Amazon EKS, EFA, and Amazon S3 and increased aggregate reinforcement learning rollout throughput by 40% for large-scale RLHF and GRPO training.
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