Bittensor’s Covenant-72B Model Signals Shift in AI Training Methods:Jensen Huang

Jensen Huang discussed decentralized AI training with Chamath Palihapitiya during the All-In Podcast, bringing attention to developments within Bittensor. The discussion pointed to a shift in how large scale AI models may be built outside traditional centralized systems.

Covenant-72B Model Marks Technical Milestone

Templar’s Covenant-72B model, described as a significant technical accomplishment, was trained with 72 billion parameters across more than 70 contributors. The process relied on permissionless coordination over standard internet infrastructure rather than dedicated data centers, marking a departure from conventional AI training methods.

Growing Interest in Distributed AI Systems

The conversation reflects rising interest in decentralized approaches that distribute computation across participants. This model suggests potential for scalable AI development while reducing dependence on concentrated computing power, positioning decentralized networks as an emerging alternative in the evolving AI landscape.

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