TL;DR · 30-second read

The Short Version

A California company called Frore Systems says it can get up to about a third more work out of the chips that run artificial intelligence, without using more electricity.

The trick is cooling. These chips get extremely hot in a few tiny spots, and when they overheat they slow themselves down. Frore places small pieces of diamond, which moves heat better than metal, right over those spots.

Why it matters: electricity is now the scarcest ingredient in building artificial intelligence. Squeezing more from the same power could mean fewer new buildings. Frore has not yet published independent test results or named customers.

Frore Systems announced LiquidJet Diamond on October 8, 2026, in San Jose, California. The product is a liquid-cooling coldplate (the metal block that sits on a chip and carries heat away into circulating fluid) with diamond wafers built into its base, positioned directly over the hottest regions of a GPU. Frore says the design cools hotspots of up to 770 watts per square centimeter and delivers 30–35% more AI tokens per watt than a conventional skived coldplate.

The company will demonstrate LiquidJet and LiquidJet Diamond cooling a 2,300-watt NVIDIA Rubin GPU, along with its LiquidJet Nexus integrated coldplate system for half-height compute trays, at the OCP Global Summit in San Jose, October 12–16, 2026.

Executive Summary

LiquidJet Diamond extends Frore’s existing LiquidJet coldplate, which uses what the company calls a 3D ultra short-loop multi-stage design customized to each GPU’s power map. Frore says the base product lowers die temperature by 12°C, worth 25% more tokens per watt, and the diamond spreader lowers hotspot temperature a further 10°C, lifting the gain to 35%. Against a skived coldplate, it claims over 150% higher maximum hotspot power density, 75% higher heat transfer efficiency, a GPU running 16–22°C cooler, and a drop-in upgrade path.

The significance lies in the framing rather than the hardware alone. Frore is arguing that in AI data centers capped by grid connections and transformer capacity, chip temperature is a capacity variable: a cooler chip produces more useful output from the same fixed power budget. If the numbers hold up under customer testing, cooling moves from a facilities cost line to a revenue lever.

The headline claim of up to 35% higher profitability, however, rests on a chain of assumptions about demand, pricing and component cost that Frore has not detailed, and the performance figures have not yet been independently validated.

Why a Few Square Millimeters Can Set a Data Center’s Output

A modern AI accelerator does not heat evenly. Some regions of the die, the slab of silicon doing the computation, run much hotter than others. A chip protects itself by slowing down when its hottest point crosses a temperature limit, a behavior known as thermal throttling, and hotter silicon also leaks more electrical current that does no useful work. Both effects mean the temperature of the worst spot on the chip, not the average temperature of the rack, can determine how much computing each watt buys.

That is the mechanism behind Frore’s numbers. The base LiquidJet design cuts die temperature by 12°C, which Frore equates to 25% more tokens per watt. Tokens are the chunks of text an AI model reads or writes, so tokens per watt is a rough measure of useful AI output per unit of electricity. Adding diamond, which conducts heat far better than copper, directly over the hotspots cuts hotspot temperature a further 10°C and lifts the claimed gain to 35%. Frore says the diamond version handles hotspot power density more than 150% higher than a skived coldplate, a statement about peak heat concentrated in a small area rather than total heat removed.

The reason that ratio matters is the constraint Frore itself names: utility interconnection (the queue to connect a site to the grid), transformer capacity and on-site power delivery cap what an AI facility can draw. In a building whose power is fixed, more tokens per watt is effectively added capacity without a new substation. Chief executive Seshu Madhavapeddy makes that case directly, saying that with 35% more tokens from the same resources, hyperscalers “would reach their growth goals faster with fewer AI Factories.” The operators most exposed are those deploying the newest, highest-power GPUs, where hotspot density climbs fastest.

From Tokens per Watt to Profit Takes Several Assumptions

The release headline promises up to 35% higher profitability; the body says tokens per watt and revenue rise 35%, “with the increased revenue flowing directly to profit.” Each step in that chain carries conditions. Extra tokens only become revenue if there are buyers for them at the same price. Extra throughput only materializes if the GPU is limited by heat rather than by memory bandwidth, networking or software. And the gain only flows straight to profit if the diamond coldplate costs no more over its life than the part it replaces, a figure Frore has not published.

The temperature figures also mix measures. The 12°C reduction refers to die temperature and the additional 10°C to hotspot temperature; their sum, 22°C, matches the top of the 16–22°C range Frore quotes for a cooler GPU, and the 30–35% tokens-per-watt range suggests results vary by chip and workload. “Up to” marks 35% as the best case. The baseline matters too: “traditional skived coldplates” covers a wide range of designs, and the size of any improvement depends on which one is being compared.

None of this makes the direction implausible. Cooler silicon generally runs faster and wastes less power, and hotspot-targeted cooling is a logical response to rising chip power. The open question is magnitude under real data center conditions, which is what customer qualification testing exists to establish.

The Harder Claim Is Manufacturing at Hyperscale

Frore positions LiquidJet between two incumbent approaches. Skiving, in which thin fins are carved from a copper block, is mature and cheap but, Frore argues, cannot deliver the precision today’s GPUs demand. Electrochemical additive manufacturing (ECAM), a form of 3D metal printing, offers design freedom but, in Frore’s telling, cannot meet hyperscaler quality, reliability, volume or timelines. Frore says it combines semiconductor-fabrication precision with high-volume manufacturing, with production operations in Taiwan.

Size is part of the pitch. Frore cites die areas ranging from 1,488 mm² for NVIDIA Blackwell Ultra to 46,225 mm² for the Cerebras Wafer-Scale Engine, roughly 30 times larger, and claims its process is the only one able to support the power, performance and size requirements of such chips. Its LiquidJet Nexus system goes further, integrating multiple coldplates and eliminating hoses, connectors and manifolds to enable half-height (½U) compute trays, which would mean fewer potential leak points and denser servers.

If those claims survive qualification, the pressure falls on suppliers of skived coldplates and on 3D-printing approaches competing for the same high-end sockets. The “seamless drop-in upgrade” claim is commercially important because it lowers switching costs, but hyperscale buyers typically qualify cooling hardware over long cycles, and a component that sits directly on a multi-thousand-dollar GPU faces a high bar for reliability evidence.

Background

Frore Systems built its reputation on AirJet, a solid-state active air-cooling chip used in consumer, industrial and Internet-of-Things devices, including a concept ultra-slim notebook unveiled with Lenovo. The company, which describes itself as an AI industry unicorn, has since moved into data center liquid cooling with LiquidJet and LiquidJet Nexus, and earlier published a report, “Chip to Chiller,” arguing that heat costs AI facilities a large share of their compute capacity.

The broader market context is a shift from air cooling to direct-to-chip liquid cooling as AI accelerators have climbed into the thousands of watts per chip. With grid interconnection and transformer capacity now limiting how fast AI data centers can grow, suppliers across the thermal stack are increasingly pitching their products on output per watt rather than on cooling alone. The OCP Global Summit, run by the Open Compute Project industry consortium, is a key venue where hyperscalers evaluate such hardware.

Sources

Source: LiquidJet Diamond Coldplates can increase AI Factory profitability by up to 35% — Frore Systems’ announcement of its diamond-spreader liquid-cooling coldplate for AI GPUs, distributed via PR Newswire.