Palihapitiya described the effort in plain terms: a large-scale language model (LLM) trained without centralized infrastructure, powered instead by a network of independent contributors. “They managed to train a 4 billion parameter LLaMA model, totally distributed, with a bunch of people contributing excess compute,” he said, calling it “a pretty crazy technical accomplishment.”
The comparison landed with a familiar analogy. “There are random people, and each person gets a little share,” Palihapitiya added, referencing the early distributed computing project that harnessed idle hardware worldwide.
Huang made clear he sees both tracks as essential. “Models are a technology, not a product,” he said, noting that most users will continue relying on polished, general-purpose systems rather than building their own from scratch.
At the same time, he pointed to industries where customization is not optional. “There are all these industries where their domain expertise… has to be captured in a way that they can control,” Huang explained, adding that “that can only come from open models.”
Technically, the model pushes boundaries. Built with 72 billion parameters and trained on roughly 1.1 trillion tokens, it leverages innovations such as compressed communication protocols and distributed data parallelism to make training viable outside traditional data centers.
Performance metrics suggest it is not merely experimental. Benchmark results place it in competition with established centralized models, a detail that helps explain why the project has drawn attention beyond crypto-native audiences.
Still, Huang’s comments suggest the real story is not disruption, but coexistence between the two. Proprietary AI systems will likely remain dominant for general users, while open and decentralized models carve out roles in specialized, cost-sensitive, or sovereignty-driven applications.
FAQ What is Bittensor’s Covenant-72B? A 72 billion-parameter language model trained through a decentralized network of contributors without centralized infrastructure. What did Jensen Huang say about decentralized AI? He said open and proprietary AI models will coexist, describing the relationship as “A and B,” not a choice between them. Why is this development important? It shows large-scale AI models can be trained outside traditional data centers, challenging assumptions about infrastructure needs. How does this affect the AI industry? It supports a hybrid future where centralized platforms and decentralized models serve different roles across industries.
