Behind plot to break Nvidia’s grip on AI
Nvidia earned its $2.2-trillion market cap by producing artificial intelligence (AI) chips that have become the lifeblood powering the new era of generative AI developers from start-ups to Microsoft, OpenAI and Google parent Alphabet.
Almost as important to its hardware is the company’s nearly 20 years’ worth of computer code, which helps make competition with the company nearly impossible. More than 4-million global developers rely on Nvidia’s Cuda software platform to build AI and other apps.
Now a coalition of tech companies that includes Qualcomm, Google and Intel plans to loosen Nvidia’s chokehold by going after the chip giant’s secret weapon: the software that keeps developers tied to Nvidia chips. They are part of an expanding group of financiers and companies hacking away at Nvidia’s dominance in AI.
“We’re actually showing developers how you migrate out from an Nvidia platform,” Vinesh Sukumar, Qualcomm’s head of AI and machine learning, said.
Starting with a piece of technology developed by Intel called OneAPI, the UXL Foundation, a consortium of tech companies, plans to build a suite of software and tools that will be able to power multiple types of AI accelerator chips, executives involved with the group said. The open-source project aims to make computer code run on any machine, regardless of what chip and hardware powers it.
“It’s about specifically — in the context of machine learning frameworks — how do we create an open ecosystem, and promote productivity and choice in hardware,” Google’s director and chief technologist of high-performance computing, Bill Hugo, said. Google is a founding members of UXL and helped determine the technical direction of the project, Hugo said.
UXL’s technical steering committee is preparing to nail down technical specifications in the first half of this year. Engineers planned to refine the technical details to a “mature” state by the end of the year, executives said.
These executives emphasised the need to build a solid foundation to include contributions from multiple companies that could also be deployed on any chip or hardware.
Beyond the initial companies involved, UXL will court cloud computing companies such as Amazon.com and Microsoft’s Azure, and other chipmakers.
Since its launch in September, UXL had begun to receive technical contributions, the executives involved said.
Intel’s OneAPI is already usable.
The second step is to create a standard programming model of computing designed for AI.
UXL plans to put its resources towards addressing the most pressing computing problems dominated by a few chipmakers, such as the latest AI apps and high-performance computing applications. Those early plans feed in to the organisation’s longer-term goal of winning over a critical mass of developers to its platform.
UXL eventually aims to support Nvidia hardware and code, in the long run.
When asked about the open source and venture-funded software efforts to break Nvidia’s AI dominance, Nvidia executive Ian Buck said: “The world is getting accelerated. New ideas in accelerated computing are coming
from all across the ecosystem, and that will help advance AI and the scope of what accelerated computing can achieve.”
The UXL Foundation’s plans are one of many efforts to chip away at Nvidia’s hold on the software that powers AI. Venture financiers and corporate dollars have poured more than $4bn into 93 separate efforts, according to custom data compiled by PitchBook.
WEAKNESS
The interest in unseating Nvidia through a potential weakness in software has ramped up in the last year, and start-ups aiming to poke holes in the company’s leadership gobbled up just over $2bn in 2023 compared with $580m from a year ago, according to the data from PitchBook.
Success in the shadow of Nvidia’s grip on AI data crunching is an achievement that few of the start-ups will be able to achieve. Cuda is a compelling piece of software on paper as it is full-featured and is consistently growing both from Nvidia’s contributions and the developer community.
“But that’s not what really matters,” said Jay Goldberg, CEO of D2D Advisory, a finance and strategy consulting firm.
“What matters is the fact that people have been using Cuda for 15 years. They built code around it.”