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How Nvidia Is Trying to Solve the Data Center Power Bottleneck

Nvidia is tracking “every single gigawatt of land, power and shell around the world, literally everything on the planet,” CEO Jensen Huang said at Goldman Sachs’ annual tech conference earlier this month. “We know where everything is.”

Why track power so intently?

Nvidia sees power availability as a key barrier to getting its AI server chips online as soon as they ship, as we’ve chronicled in this column over and over. While securing power is the first step to any AI data center project, even before getting the land or chips, developers that work with Google, Microsoft, Oracle and others fear a glut of unused chips due to an eventual power shortage.

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Apple Considers Return to Server Market, Has Talked With Nvidia to Use Network Tech

Apple has been working on plans for an enterprise server using its own chips that could incorporate networking equipment from Nvidia, people familiar with the project said, as the Mac maker looks to capitalize on surging interest in its computers for AI.

Apple has been developing the server with plans to sell it to AI developers, other businesses and governments, the people said. The product under consideration would come in two versions—a smaller one with two of Apple’s planned M8 Ultra chips clustered together and a bigger one with four M8 Ultra chips. The M-series Ultra chips are Apple’s highest-performance chips and power products including the Mac Studio.

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Gimlet's Three-Tranche Deal Reveals AI Funding Frenzy

Gimlet Labs said late last week it had raised $300 million at a valuation of about $3 billion, in a round led by new investor Andreessen Horowitz. Today we have unreported details on how that round, which boosted its valuation by a whopping 16 times from its last PitchBook valuation, came together.

The three-year-old startup, which helps AI developers run models using different AI chips simultaneously, raised the round after the company’s executives told some potential investors that based on signed contracts, OpenAI was likely to spend more than $100 million a year on its services, according to a person familiar with the company. Such outlays, if they transpire, would be significant given it just emerged from stealth in October. 

This windfall would be in the future, however. “We have explored possibilities with Gimlet but we are not a paying customer,” said an OpenAI spokesperson.

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Why Nvidia Is Trying To Develop The World’s Best Open-Source AI Models

Nvidia is doubling down on its open-source efforts, pouring investment into an ambitious new in-house AI model that it hopes will drive demand for its hardware—but that could also compete with customers and partners.

Nvidia is aiming for the new model, the biggest of a family that it is calling Nemotron 4, to be on par in its performance with the best open-source AI models in the world, according to several people who work on Nemotron. The research paper detailing Nvidia’s last big model had 570 authors, and employees said Nemotron 4 would involve even more people. “Everyone just wants to be involved at this point,” one former employee said.

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Nvidia to Invest Up to $3 Billion in Blackstone-Backed Power Firm Behind Stargate

Nvidia has agreed to invest $2 billion into Lancium, the power infrastructure developer behind the OpenAI and Oracle AI campus in Texas, and has agreed to commit another $1 billion as the developer secures additional planned power, three people familiar with the transaction said. The deal values Lancium and its portfolio of land and power connections at around $10 billion in enterprise value, which includes the investment and its debt.

The $2 billion would give Nvidia a roughly 20% stake in the Blackstone-backed power developer. Nvidia’s stake could grow to around 30% with the additional $1 billion once more of Lancium’s campuses hit certain thresholds, including grid hookups, the people said. The deal is intended to help Nvidia secure multiple gigawatts of pending power rights for its chip customers’ data projects at a time when energy for AI data centers is becoming harder to come by.

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