AWS reports 40% more RL throughput

AWS reports 40% higher aggregate rollout throughput for a super-sparse Mixture-of-Experts reinforcement-learning workload after enabling DeepEP over EFA across 48 P5en instances—16 for policy training and 32 for inference. In its reference architecture, AWS frames the result as one internal workload with pinned software versions, not a universal GPU claim, and says rollout workers can use Spot capacity because interrupted inference jobs can be requeued. RL platform teams should benchmark communication and worker balance before buying more accelerators, extending the archive’s Strands benchmark-cost analysis.