Hardware & Compute

Nvidia wants to cut data center water use, but that’s not the same as fixing AI’s water problem

Nvidia announced a new cooling system that cuts water use inside the data center. But it does nothing to address AI's biggest water use — fossil fuel power plants.

Get smart on it

Nvidia has developed a warm-water cooling system that the company claims eliminates water use inside data centers by recirculating coolant in a closed loop rather than consuming fresh water. However, this solution only addresses a portion of data center water consumption because it ignores water used outside the facility walls, particularly in electricity generation and chip manufacturing, which can double or triple the total water footprint. Fossil fuel power plants that supply about half of all data center power are major water consumers, using between 1.17 liters per kilowatt-hour for natural gas plants and 2.2 liters per kilowatt-hour for coal plants. Because natural gas and coal are expected to provide more than 40 percent of new electricity needed for data centers through 2030, significant water consumption will persist regardless of improvements made within data center facilities themselves.

The biggest AI stories, once a day

One short email a day with the stories that matter. Free. Unsubscribe with one click.

Relevant courses

Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.

Browse AI courses on Maven
Related news
Hardware & ComputeOpen story →

Google Unveils TPU 8i Max and TPU 8tx: A Dual-Chip TPU and a Confidential TPU

Google has added two new systems to its eighth-generation TPU lineup. TPU 8i Max targets massive context windows and rack density. TPU 8tx targets secure, confidential inference. Google TPU 8i Max is a dual-chip package built from 2 TPU 8i chips. Both arrive alongside TPU 8t and TPU 8i, announced in April. TL;DR Size: TPU 8i Max = 2 TPU 8i chips in 1 package. TPU 8tx = TPU 8t silicon plus 5th Gen AMD EPYC hosts. Runs on: Google Cloud TPU systems. Performance: 8i Max doubles TPU 8

Also reported by Data Center Knowledge

Hardware & ComputeOpen story →

IBM Announces IBM Quantum Nighthawk r3: A New Processor to Support 10k Quantum Operations on 520 Programmable Qubits

IBM has announced IBM Quantum Nighthawk r3, its next superconducting quantum processor. It is designed to run 10,000 quantum operations accurately across 520 programmable qubits. In 2027, IBM plans to link 2 r3 chips into a single 1,040-qubit system. For developers, the announcement is speed. The current Nighthawk r2 already runs workloads up to 19x faster than IBM’s earlier processors. TL;DR Size: Nighthawk r3: 520 programmable qubits, 2,034 physical qubits, 10 l-couplers. Nightha

Hardware & ComputeOpen story →

Why the data center you dread might never even be built

The data center boom is running into serious real-world obstacles, from widespread local opposition and permitting delays to a scramble for electricity. Before those challenges even come into play, though, there’s a more basic problem: A lot of the projects being announced may never get built in the first place. With billions of dollars in AI infrastructure […]

Hardware & ComputeOpen story →

How backlash and red tape continue to slow data centers post-Blue Wave

The data center boom is running into serious real-world obstacles, from widespread local opposition and permitting delays to a scramble for electricity. Before those challenges even come into play, though, there’s a more basic problem: A lot of the projects being announced may never get built in the first place. With billions of dollars in AI infrastructure […]

Hardware & ComputeOpen story →

AMD Launches Epyc SP7, Helion as AI Rack Demand Rises

AMD has begun shipping sixth-gen Epyc servers and is rolling out Helion AI racks, signaling a bigger push into enterprise and agentic AI infrastructure.

Hardware & ComputeOpen story →

Google broadens its AI hardware portfolio with two new specialized chips

Google's new tensor processing units (TPUs) broaden its chip portfolio to more use cases: confidential model training and more memory capacity.