Argentum AI is deploying new capital from an oversubscribed round led by Kraken to build a decentralized network that lets anyone monetize idle processing power, creating a new class of “GPU entrepreneurs.”
Summary
- Kraken led an oversubscribed pre-seed round in Argentum AI, a Menlo Park startup building a decentralized compute marketplace.
- Argentum AI enables organizations to bid for computing power on-chain, letting providers monetize idle GPUs and data center capacity.
- The platform supports AI training, 3D rendering, scientific simulations, and more, blending human oversight with AI-driven task matching.
According to a press release shared with crypto.news on Oct. 1, the Menlo Park-based startup secured an oversubscribed pre-seed round. The capital infusion was led by crypto exchange Kraken, with participation from Banyan Ventures and angel investors Victor Morganstern and Todd Bensen.
Argentum AI said it will use the funds to accelerate the development of its core platform, an open marketplace that uses blockchain for real-time bidding and settlement of computational tasks.
Inside Argentum AI’s marketplace model
Argentum AI describes itself as a “human-friendly, AI-powered compute marketplace,” and its model blends decentralized infrastructure with machine intelligence to improve how computational jobs are matched, priced, and executed.
Per the release, Argentum AI functions as an open exchange where organizations can post computing tasks, ranging from AI model training and 3D rendering to big data analysis and digital twin simulations. Providers, whether individuals with a spare GPU or data centers with large clusters, bid to execute those tasks. Settlement occurs on-chain, with Ethereum smart contracts holding funds in escrow until successful completion
Rather than positioning AI as a replacement for human input, the platform emphasizes collaboration. Clients define requirements and oversee decision-making, while an embedded AI assistant learns from completed tasks to recommend better resource matches and pricing strategi
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Author: Brian Danga
