For modern rack-scale systems like Nvidia’s NVL72s, that’s an entire rack and change that could safely be installed within the same power budget, and indeed, that’s just what the company’s example shows. And across those five systems, the amount of reserve power allocated for peaks can be much lower.
At the rack level, DSX MaxLPS offers further flexibility through workload-specific power profiles. Much like the quiet, balanced, and high-performance power modes that client PC users are familiar with, Nvidia has produced rack-level power profiles that can be assigned to systems performing example workloads like inference, training, and more general memory-bound or compute-bound tasks.
As we noted, Nvidia didn’t share measured Rubin power or performance-per-watt results, but it has characterized the benefits of MaxLPS for prior-generation systems running inference workloads to prove the concept.
For a Grace Blackwell GB300 system running DeepSeek-R1, Nvidia says the past fixed-peak regime would have assumed a 1400W GPU TGP and an estimated rack power of 136kW. Applying MaxLPS, however, the typical GPU TGP under this workload falls to 1000W, and the total rack power falls to 101kW, all without affecting delivered performance.
That less conservative envelope translates directly into higher performance per watt, larger numbers of racks that can be installed within the same facility, and ultimately more tokens that can produce revenue for the data center operator or its tenants.
Nvidia further notes that designing a data center with MaxLPS from the start grants an operator greater flexibility over the life of the installation. For example, if a site starts as a training-focused facility outfitted with cutting-edge hardware, each installed system is likely to need a greater share of the available site power for that more intense workload, and so an operator might not want to populate every available floor space for those racks from the get-go.
But later in the life cycle, as training shifts to new generations of hardware and older systems transition into inference roles, the power demands of each GPU and rack will fall, and so a facility with dynamic power provisioning would be able to free up capacity that can then be used to install more hardware within the same facility and to generate more profitable tokens.
Another major component of MaxLPS in data centers deploying Vera Rubin hardware is the use of higher liquid coolant temperatures for the exclusively liquid-cooled Rubin NVL72 racks. Those systems are designed to work with 45 °C inlet coolant temperatures, much higher than for past liquid-cooled systems. We learned more about this “dry cooling” approach during our visit to Nvidia’s Vera Rubin proving grounds earlier this year.
The use of this higher coolant temperature for Rubin installations is important because the mechanical chillers used to shed waste heat in non-evaporative systems also consume a large portion of the site power budget – as much as 40% for past installations, Nvidia says. As with static provisioning for servers, the company notes that those chillers have traditionally been sized for the worst-case scenario that a facility might face, even if they’re operating well below that capacity for much of the year.
Again, this approach strands power that could be dynamically reallocated to compute given the proper operating conditions and site-level monitoring and management. Those chillers might still need to run during the hottest parts of the year, but outside of those conditions, the higher coolant temperature generally enables more power to be put to productive use, improving a site’s power usage effectiveness (PUE) figure, all else equal.
Power for AI data centers, whether generated by public utilities or behind the meter using alternative power sources, is expected to remain one of the most critical constraints for those facilities for the foreseeable future, and we heard that concern from multiple presenters during Hot Chips.
Nvidia’s DSX MaxLPS approach looks ready to provide the building blocks needed for dynamic allocation of that resource to extract the maximum possible performance per watt from Rubin facilities, and it reflects a comprehensive concern for the interplay of power and achievable performance that only seems likely to grow in importance going forward.
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TOPICS Nvidia See all comments (0) Jeffrey Kampman Senior Analyst, Graphics As the Senior Analyst, Graphics at Tom's Hardware, Jeff Kampman covers everything that has to do with graphics cards, gaming performance, and more. From integrated graphics processors to discrete graphics cards to the hyperscale installations powering our AI future, if it's got a GPU in it, Jeff is on it.
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- https://www.tomshardware.com/tech-industry/data-centers/SPONSORED_LINK_URL
- https://www.tomshardware.com/tech-industry/data-centers/hot-chips-2026-nvidia-touts-benefits-of-its-dsx-maxlps-site-power-management-approach-tech-allows-for-more-compute-from-fixed-data-center-power-budgets#main
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