Chill Factor: NVIDIA Blackwell Platform Boosts Water Efficiency by Over 300x

Traditionally, data centers have relied on air cooling - where mechanical chillers circulate chilled air to absorb heat from servers, helping them maintain optimal conditions. But as AI models increase in size, and the use of AI reasoning models rises, maintaining those optimal conditions is not only getting harder and more expensive - but more energy-intensive.

While data centers once operated at 20 kW per rack, today's hyperscale facilities can support over 135 kW per rack, making it an order of magnitude harder to dissipate the heat generated by high-density racks. To keep AI servers running at peak performance, a new approach is needed for efficiency and scalability.

One key solution is liquid cooling - by reducing dependence on chillers and enabling more efficient heat rejection, liquid cooling is driving the next generation of high-performance, energy-efficient AI infrastructure.

The NVIDIA GB200 NVL72 and the NVIDIA GB300 NVL72 are rack-scale, liquid-cooled systems designed to handle the demanding tasks of trillion-parameter large language model inference. Their architecture is also specifically optimized for test-time scaling accuracy and performance, making it an ideal choice for running AI reasoning models while efficiently managing energy costs and heat.

Liquid-cooled NVIDIA Blackwell compute tray. Driving Unprecedented Water Efficiency and Cost Savings in AI Data Centers Historically, cooling alone has accounted for up to 40% of a data center's electricity consumption, making it one of the most significant areas where efficiency improvements can drive down both operational expenses and energy demands.

Liquid cooling helps mitigate costs and energy use by capturing heat directly at the source. Instead of relying on air as an intermediary, direct-to-chip liquid cooling transfers heat in a technology cooling system loop. That heat is then cycled through a coolant distribution unit via liquid-to-liquid heat exchanger, and ultimately transferred to a facility cooling loop. Because of the higher efficiency of this heat transfer, data centers and AI factories can operate effectively with warmer water temperatures - reducing or eliminating the need for mechanical chillers in a wide range of climates.

The NVIDIA GB200 NVL72 rack-scale, liquid-cooled system, built on the NVIDIA Blackwell platform, offers exceptional performance while balancing energy costs and heat. It packs unprecedented compute density into each server rack, delivering 40x higher revenue potential, 30x higher throughput, 25x more energy efficiency and 300x more water efficiency than traditional air-cooled architectures. Newer NVIDIA GB300 NVL72 systems built on the Blackwell Ultra platform boast a 50x higher revenue potential and 35x higher throughput with 30x more energy efficiency.

Data centers spend an estimated $1.9-2.8M per megawatt (MW) per year, which amounts to nearly $500,000 spent annually on cooling-related energy and water costs. By deploying the liquid-cooled GB200 NVL72 system, hyperscale data centers and AI factories can achieve up to 25x cost savings, leading to over $4 million dollars in annual savings for a 50 MW hyperscale data center.

For data center and AI factory operators, this means lower operational costs, enhanced energy efficiency metrics and a future-proof infrastructure that scales AI workloads efficiently - without the unsustainable water footprint of legacy cooling methods.

Moving Heat Outside the Data Center As compute density rises and AI workloads drive unprecedented thermal loads, data centers and AI factories must rethink how they remove heat from their infrastructure. The traditional methods of heat rejection that supported predictable CPU-based scaling are no longer sufficient on their own. Today, there are multiple options for moving heat outside the facility, but four major categories dominate current and emerging deployments.

Key Cooling Methods in a Changing Landscape

Mechanical Chillers: Mechanical chillers use a vapor compression cycle to cool water, which is then circulated through the data center to absorb heat. These systems are typically air-cooled or water-cooled, with the latter often paired with cooling towers to reject heat. While chillers are reliable and effective across diverse climates, they are also highly energy-intensive. In AI-scale facilities where power consumption and sustainability are top priorities, reliance on chillers can significantly impact both operational costs and carbon footprint.

Evaporative Cooling: Evaporative cooling uses the evaporation of water to absorb and remove heat. This can be achieved through direct or indirect systems, or hybrid designs. These systems are much more energy-efficient than chillers but come with high water consumption. In large facilities, they can consume millions of gallons of water per megawatt annually. Their performance is also climate-dependent, making them less effective in humid or water-restricted regions.

Dry Coolers: Dry coolers remove heat by transferring it from a closed liquid loop to the ambient air using large finned coils, much like an automotive radiator. These systems don't rely on water and are ideal for facilities aiming to reduce water usage or operate in dry climates. However, their effectiveness depends heavily on the temperature of the surrounding air. In warmer environments, they may struggle to keep up with high-density cooling demands unless paired with liquid-cooled IT systems that can tolerate higher operating temperatures.

Pumped Refrigerant Systems: Pumped refrigerant systems use liquid refrigerants to move heat from the data center to outdoor heat exchangers. Unlike chillers, these systems don't rely on large compressors inside the facility and they operate without the use of water. This method offers a thermodynamically efficient, compact and scalable so

LINK:	https://blogs.nvidia.com/blog/blackwell-platform-water-efficiency-liqu...
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