Quick take:
- The platform aggregates global compute enabling researchers to collaborate and train “state-of-the-art” AI models through distributed training.
- Prime Intellect also allows researchers to share the ownership and benefits associated with the AI models they helped train.
- The company will use the fresh capital to accelerate the development of its platform.
Prime Intellect has raised $5.5 million in a funding round backed by Distributed Global, CoinFund, Compound VC, Collab + Currency and Protocol Labs.
The fundraising also attracted participation from several angel investors including Andrew Kang of Mechanism Capital, Bool Capital from Wormhole, EigenLayer’s Robert Drost, Riva Tez of LayerZero, Worldcoin’s Dcbuilder, Scott Moore of Gitcoin, DCF God and Gmoney, among others.
Prime Intellect is building a decentralised AI infrastructure platform enabling collective ownership and governance of AI models. According to the announcement, its platform aggregates global compute enabling researchers to collaborate and train “state-of-the-art” AI models through distributed training.
Researchers are able to share the ownership and benefits associated with the AI models they help train.
“Through Prime Intellect, anyone will be able to contribute compute, capital and code to collaboratively train open AI models and share in their ownership and benefits. We envision this enabling a new wave of open AI progress, from community-driven language models to scientific breakthroughs,” co-founders Vincent Weisser and Johannes Hagemann wrote in a blog post on the company’s website.
Prime Intellect wants to build a platform that allows anyone to contribute resources including compute, capital and code, and enable globally distributed training across clusters.
According to Prime Intellect’s post on X, its platform is currently live in beta and aggregating supply from leading GPU providers, offering the most cost-effective, scalable on-demand compute.
“We’re preparing to provide access to clusters of 16-128+ GPUs and developing a framework for distributed training across clusters,” a post on X reads.
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