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Press releaseGlobeNewswire· March 24, 2026

0G Labs Trained World's Largest Decentralized AI Model at 107B Parameters in 2025 - Eight Months Before This Week's Industry Headlines

View original at globenewswire.com
0G Labs Trained World's Largest Decentralized AI Model at 107B Parameters in 2025 - Eight Months Before This Week's Industry Headlines San Francisco, CA, March 24, 2026 (GLOBE NEWSWIRE) -- While the crypto industry celebrated Bittensor's Covenant-72B this week as a breakthrough in decentralized AI training, 0G Labs had…
Opening lines of the source · GlobeNewswire · short snapshot — read the full document at the original

What we drew from this source

The claims Via News extracted from this document. We point to the source; we don't replace it.

  • While the industry celebrated Bittensor's 72B model this week, 0G had already trained 107B parameters in July 2025 - 48% larger, 8 months earlier

    60% confidence
  • This isn't about breaking records, it's about building AI as a public good

    60% confidence
  • We proved decentralized infrastructure can train a 107 billion parameter model in 2025, before anyone else

    60% confidence
  • 0G's approach achieves approximately 95% cost reduction compared to centralized GPU cluster training

    60% confidence
  • This week's headlines celebrating 72 billion parameters as a milestone missed that 0G had already operated at significantly larger scale

    60% confidence
  • 0G set the benchmark for decentralized AI training on standard consumer bandwidth

    60% confidence
  • The industry is finally paying attention to decentralized AI

    60% confidence
  • Distributed, open-source AI training is complementary to centralized approaches and will play a growing role in frontier model development

    60% confidence

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Both facts record the same metric (shares_outstanding) for ING Group at the identical observation date (2025-12-31). FACT A states 2,902,437,688 shares; FACT B states 2,902 million shares (2,902,000,000). The difference is 437,688 shares (~0.015%). This is a genuine value conflict, though the discrepancy appears to result from FACT B rounding to the nearest million while FACT A provides the precise count.
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