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/News/Frore Systems raises $143M as AI chips drive demand for liquid cooling

Frore Systems raises $143M as AI chips drive demand for liquid cooling

Frore Systems reported a $143M raise and highlighted its LiquidJet chip cooling for AI data centers, built to handle very high heat from new GPUs.

About 2 hours ago•Funding & Acquisitions

In short: Frore Systems says it raised $143 million and is pushing its LiquidJet liquid cooling system as AI data centers struggle with hotter chips.

What happened

TechCrunch reported that Frore Systems has raised $143 million and is now valued at about $1.64 billion. The report also says NVIDIA CEO Jensen Huang urged Frore to build liquid cooling for chips.

That specific claim about Huang’s urging is not confirmed by other publicly available reporting referenced in the provided background. The same is true for the $143 million figure, which is not confirmed by the other sources summarized in the background.

What can be confirmed is Frore’s product timeline and its stated goal. Frore announced LiquidJet in October 2025 as a “direct-to-chip” liquid cooling system for AI data centers, meaning it is designed to move heat away from the chip itself, not just the surrounding hardware.

Frore said LiquidJet was built to work with NVIDIA’s highest power processors, starting with the 1,400 watt Blackwell Ultra GPU. (A GPU is a powerful chip often used for AI, and higher wattage usually means more heat, like a brighter light bulb getting hotter.) Frore also reported performance claims such as higher cooling capacity and lower resistance to fluid flow compared with traditional cold plates.

In December 2025, Frore said it improved its design and increased cooling performance for Blackwell Ultra, and it also cited over 50% improvement for NVIDIA’s 1,950 watt Rubin GPU.

Why it matters

AI services run in data centers, and those centers use lots of electricity and produce lots of heat. Better cooling can help data centers pack in more computing power without overheating, which can affect costs, reliability, and how quickly new AI systems can be deployed.

Source: TechCrunch AI

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