Omen AI's plan to optimize data centers is all wet
The AI-driven demand for compute power has data centers looking to squeeze more from every rack of GPUs. The liquid for liquid-cooled chips is a mixture of water and a substance that inhibits bacteria growth.
Key Takeaways
- Omen AI raised a $31 million Series A to monitor chip coolant and stop bacterial outbreaks in data centers.
To run the chips hotter, data center managers can change the mix to include more water, which absorbs heat better, but leads to nasty contamination that clogs the flow.
- Today, Omen AI said it raised a $31 million Series A round, led by Nava Ventures and including participation from CRV, Vanderbilt University, Mann+Hummel, Starhill Holdings, and Hard Launch Capital, as well as personal investments from executives at Bridgestone, GM, Johnson Controls, and TensorWave.
Laberge founded his first company in 2020 when he was 14, raising $3 million to install sensors on construction equipment and ultimately dropping out of high school.
- Caterpillar dealerships were a key early customer for Omen's heavy vehicles business, but Cat is also a major supplier of gas-powered turbines and generators to provide on-premises power for data centers.
It didn't take long for Omen to see where the wind was blowing.
- "It's rare to see such a young founder who has the respect of established, large corporations in a space that moves a bit more slowly," said Cory Rellas, a partner at Nava Ventures who sits on Omen's board.
"For Omen in particular, much of our diligence came through our introductions with large customers which quickly validated their approach.
- The key tech advances that unlocked this approach are recent improvements in both optical technologies and signal processing software.
Stats & Key Facts
- #Omen AI raised a $31 million Series A to monitor chip coolant and stop bacterial outbreaks in data centers.
- #Omen AI raised a $31 million Series A to monitor chip coolant and stop bacterial outbreaks in data centers.
- #Today, Omen AI said it raised a $31 million Series A round, led by Nava Ventures and including participation from CRV, Vanderbilt University, Mann+Hummel, Starhill Holdings, and Hard Launch Capital, as well as personal investments from executives at Bridgestone, GM, Johnson Controls, and TensorWave.
- #Laberge founded his first company in 2020 when he was 14, raising $3 million to install sensors on construction equipment and ultimately dropping out of high school.
To run the chips hotter, data center managers can change the mix to include more water, which absorbs heat better, but leads to nasty contamination that clogs the flow. To solve that, they flush the system, which can mean shutting down a rack for five or six hours at a potential cost of millions of dollars. Omen AI has a solution: A tiny spectrometer that can monitor that fluid health in real time, spotting bacterial growth before it becomes a massive problem.
"You're not risking huge amounts of downtime because you have no insight into what's going on chemically," explains CEO and founder Zach Laberge. Today, Omen AI said it raised a $31 million Series A round, led by Nava Ventures and including participation from CRV, Vanderbilt University, Mann+Hummel, Starhill Holdings, and Hard Launch Capital, as well as personal investments from executives at Bridgestone, GM, Johnson Controls, and TensorWave. Laberge founded his first company in 2020 when he was 14, raising $3 million to install sensors on construction equipment and ultimately dropping out of high school.
(His father and mother, a former Minister of Education for Ontario, were supportive of his plan to carve his own path. ) After that startup shut down, Laberge started Omen in 2024, with the idea of focusing on fluid systems as the key to enabling construction machinery to be smart enough to know when it needed to be fixed. The idea was to replace the time-consuming process of extracting samples and sending them to a lab with real-time awareness.
For more details please read the original article at TechCrunch AI.
Continue Learning
Comments
Sign in to join the conversation