Agreement by Construction: Multi-Agent RL for Datacenter Network Power Management

Modern datacenters (DC) face escalating energy demands, with power consumption becoming a critical bottleneck for computational scaling. To mitigate this, we introduce LinkRL, a decentralized multi-agent reinforcement learning framework for managing power in DC networking. Our agent learns the optimal timing windows to toggle network links between active and low-power states. Specifically, we target the substantial energy these links consume while idle during computation phases. Networking infrastructure accounts for approximately 20% of the total DC power, and the inefficiency of idle links presents a significant optimization opportunity. In LinkRL, we train agents to predict traffic patterns, enabling preemptive sleep and wake-up. We guarantee agreement between agents managing a shared link, by construction and without communication, by restricting each agent's input to features both ends observe identically. We train our framework on real-world LLM training workloads using a high-fidelity networking simulator that serves as NVIDIA's primary platform for design, verification, and benchmarking of its networking products. LinkRL achieves 10.9% datacenter power savings while maintaining latency, outperforming baselines. The same policy transfers to other distributed-compute workloads without retraining. Reallocating these savings to compute yields an 8.9% GPU performance gain. LinkRL is integrated into the design of NVIDIA's next-generation networking hardware.