Maximizing AI Factory Performance per Watt with NVIDIA DSX MaxLPS

Summary

Power-constrained AI factories measure efficiency by AI output per watt, because facility overhead, rack losses, and operational inefficiency reduce the power available to compute. In an illustrative 100 MW budget, 60% reaches AI load, while static rack provisioning can strand headroom that another rack could use. NVIDIA DSX MaxLPS addresses this with dynamic power allocation, workload-profile power optimization, and 45° C thermal and site design, including warm-water liquid cooling. Its Dynamic Power Software, currently in Developer Preview, models topology from utility to GPUs and reallocates unused capacity within policy-defined budgets, with optional DSX Exchange integration. For Vera Rubin NVL72, NVIDIA projects up to 40% more Rubin GPU capacity within the same power budget, while validation compares unmanaged and managed runs using throughput, latency, errors, power, utilization, and compliance.

Takeaways

1. 1

   In the illustrative 100 MW power-budget waterfall, 20 MW goes to facility overhead, 10 MW to rack losses, and 10 MW to operational inefficiency, leaving 60 MW for AI load.

2. 2

   Dynamic Power Software continuously compares allocated power with actual consumption and can reallocate unused headroom within managed groups without changing the site power envelope.

3. 3

   NVIDIA projects that MaxLPS combined with data-center power planning can fit up to 40% more Rubin GPU capacity in the same power budget for Vera Rubin NVL72 AI factories.