NVIDIA DSX MaxLPS Overview

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Design and operate AI factories for more throughput per megawatt

Up to 40% more GPUs within the same site-power envelope

NVIDIA DSX MaxLPS is an AI factory design and operating framework that coordinates facilities and site design, NVIDIA Dynamic Power Software (DPS), and advanced performance-per-watt techniques to help NVIDIA Cloud Partners (NCPs) and other AI factory operators maximize performance per watt within a fixed power budget. Planning AI factories for MaxLPS can also make more GPU capacity available to tenants and increase throughput per megawatt.

Select one or more MaxLPS capabilities to reveal where they apply in the stack, then hover over or select a highlighted layer capability to view related resources.

DSX MaxLPS™

Platform Software
Infrastructure Software
Hardware
Facilities Infrastructure

This diagram maps MaxLPS core features and capabilities to the Facilities Infrastructure, Hardware, Infrastructure Software, and Platform Software layers of the broader DSX stack. See the DSX documentation landing page for the overall DSX architecture diagram and NVIDIA's five-layer AI factory model for additional context.

MaxP and MaxQ are static GPU-level power settings: MaxP is the highest GPU power setting, while MaxQ targets the best application performance per watt. MaxLPS extends optimization across the AI factory by combining facilities and site design, DPS for dynamic power allocation, and advanced performance-per-watt techniques within a fixed site power envelope.

Select MaxP, MaxQ, or MaxLPS to update the GPU capacity and AI factory outcome within the same fixed power budget.

Deliver more inference performance per watt from a fixed power budget

Compare the MaxP, MaxQ, and MaxLPS reference modes. Selecting a mode updates the GPU capacity and token throughput relative to MaxP for the illustrative one megawatt inference scenario.

Use the left and right arrow keys to move between modes. Home and End move to the first and last mode.
SELECTED POWER DESIGN APPROACH

MaxLPS

GPU capacity per fixed 1 MW power basis400 GPUs
AI FACTORY OUTCOME
Token throughput relative to MaxP1.35×

Illustrative Vera Rubin NVL72 inference scenario from Sizing DSX AI Factory Infrastructure for MaxLPS (NVOnline #1161311). Values assume PUE 1.1. Vera Rubin testing remains in progress.

The capacity comparison above shows the potential AI factory outcome. Realizing this potential requires planning for the full MaxLPS target, including a facility design that supports 45°C cooling, even when day-one workloads with higher GPU-power requirements, such as training, offer less headroom than inference. Planning the AI factory to support the power, cooling, and network capacity of that target gives operators flexibility to expand quickly as the workload mix changes over time.

The next three tabs show how MaxLPS applies these connected optimizations at the site, rack, and workload levels: facilities and site-design innovations help make more of the fixed site-power envelope available to IT; Dynamic Power Software manages available IT power, including reclaiming unused allocation; and workload performance-per-watt optimization makes managed capacity more productive.