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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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EPKINLY Regulatory-Clinical Success Cascade
High probability of expanded label indications, additional combination approvals, and competitive positioning strength in follicular lymphoma market. Predicts positive commercial uptake and potential accelerated review for related indications.
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JPMorgan Chase & Co.
Both facts record the same attribute (net_income) for JPMorgan Chase & Co. in the identical fiscal period (Q1 2026) and observation date (2026-03-31), but report values that differ by approximately 1 billion times: $16,494,000,000 vs $16.49. These cannot both be true simultaneously. The discrepancy suggests either a unit mismatch (e.g., one is total net income, the other earnings per share mislabeled as net_income), a decimal point error, or data entry corruption. For the same entity, attribute, and time period, only one value can be correct.
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