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0G Retrains 107B Model in Public as Decentralized AI Enters a New Phase

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with little attention.

0G says it crossed an important threshold months ago. Now it is retraining the same model in public, with the goal of showing what decentralized AI can actually deliver and why its earlier result deserved more attention.

In July 2025, 0G trained a 107 billion parameter model called DiLoCoX-107B with China Mobile. The research later appeared on arXiv after peer review. According to the paper, the system reached 357 times better communication efficiency than traditional AllReduce methods. Even so, the result barely landed in the market.

The team says the timing worked against it. Mid-2025 crypto attention was fixed on mainnet launches and token stories, while technical results drew far less interest. The work was serious, but it did not get much traction outside a small circle following the field closely.

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Now, with decentralized AI back in focus, 0G wants to bring the result back into view.

A public retraining effort

This time, the company is putting the retraining process out in the open.

0G plans to document each stage, including checkpoints, convergence metrics, and data sourcing. It also says the run will be verified through Trusted Execution Environments using zerogAuth. Once the work is complete, the model weights will be open sourced.

Ultimately, 0G wants to show that decentralized AI can be audited, reproduced, and verified in a way most closed systems cannot match.

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More than a parameter race

A lot of AI coverage still revolves around parameter counts. Bigger numbers attract attention, but 0G argues that a model’s value comes from the full system around it.

For the team, the real test starts with training and continues through verification, storage, serving, and integration into working products.

One of the main technical points is communication efficiency. DiLoCoX uses pipeline parallelism, a dual optimizer policy for local and global updates, a one-step delay overlap mechanism, and adaptive gradient compression. In plain terms, the design cuts the amount of communication needed during distributed training, which is often where these systems slow down.

0G also puts the model inside a full stack that includes onchain verification, decentralized storage, data availability, inference, and settlement. The result is a working environment rather than a one-off research demo.

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Verification is another part of the pitch. With Trusted Execution Environments, users can check more than the existence of a model. They can inspect how it was trained and what data went into the process. For decentralized AI, that changes the trust model in a meaningful way.

The real story is bandwidth

According to 0G, the most important part of the DiLoCoX-107B result was the way the model was trained.

The team says the 107B model ran on standard one gigabit per second internet connections rather than specialized data center setups. That point goes straight at one of the biggest assumptions in AI, namely that frontier training requires rare and expensive networking conditions.

If that holds up over time, the impact could be substantial. Lower technical requirements open the door to far more participants, from research groups to companies and public institutions. In that setup, coordination becomes the main challenge, and decentralized systems are built for exactly that kind of problem.

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A different cost model

0G also says its system cuts costs by about 95% compared with centralized alternatives.

The company attributes that reduction to the removal of expensive centralized overhead rather than cheaper hardware. If those numbers hold in real-world use, advanced model training becomes accessible to far more organizations, including universities, enterprises, and governments that do not have the budget for hyperscale AI spending.

That could change who gets to build serious models in the first place.

Can decentralized AI compete?

Skeptics have long argued that decentralized AI cannot keep up on performance. 0G believes the old tradeoff is starting to weaken.

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As results improve and costs fall, the discussion becomes less about ideology and more about output. Can the system train strong models, verify them, and do it at a price point more teams can afford?

Open participation still comes with real risk. Distributed training can expose systems to data poisoning, gradient manipulation, and uneven contributor quality. 0G says it addresses those issues with architectural safeguards, anomaly detection, and cryptographic verification.

The point is not perfect safety. The point is making failures visible and traceable.

What verifiable AI actually means

For 0G, verifiable AI is about replacing trust by reputation with trust by inspection.

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Instead of taking a provider at its word, users get a way to independently check how a model was trained and how it operates. That idea has obvious value in areas where accountability carries real weight, including finance, healthcare, and government.

This is where decentralized AI starts to stand apart, with systems people can inspect rather than simply trust.

From research demo to working system

The decentralized AI field has come a long way in a short time. Early proof-of-concept work is giving way to systems designed for training, verification, storage, inference, and economic settlement inside one environment.

0G wants DiLoCoX-107B to stand as proof of that progression. The public retraining effort is as much about process as performance. The company is trying to show that decentralized AI can produce serious models while staying open to inspection.

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The road ahead

Larger models are still on the horizon. 0G believes models in the hundreds of billions, and eventually trillions, are within reach.

The next stage depends less on a single scientific leap and more on better coordination and stronger network participation. In decentralized AI, organization may prove just as important as compute.

The retraining of DiLoCoX-107B is an attempt to reopen a conversation 0G believes the market missed the first time. It is also a test of whether open, verifiable AI can win attention on the strength of results rather than hype.

For now, the company is betting that public retraining, transparent documentation, and open access will give decentralized AI a stronger footing in the next round of competition.

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Ripple Says Stablecoins Will Drive Enterprise Crypto Adoption

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Crypto Breaking News

Ripple CEO Brad Garlinghouse framed stablecoins as the crypto sector’s potential “ChatGPT moment” for enterprise payments, arguing that faster, more efficient settlements could accelerate real-world adoption among large corporations. In an interview with FOX Business on Friday, he said boards of directors and chief financial officers at Fortune 500 and Fortune 2000 companies are already asking treasurers how stablecoins could fit into their operations, signaling a shift from experimentation to formal strategy.

Garlinghouse described the move as an “unlock” for corporate finance, arguing that giving treasurers a credible on-chain settlement option could accelerate the broader adoption of blockchain-enabled services. He suggested stablecoins could serve as an entry point to a wider ecosystem of digital-asset tools used by enterprises, beyond just payments.

Bloomberg Intelligence has projected that stablecoin payment flows could grow at roughly an 80% compound annual rate to about $56.6 trillion by 2030, underscoring the potential scale if regulation and infrastructure align with demand.

Garlinghouse also highlighted the sheer volumes already moving through stablecoins. He noted that last year stablecoins processed more than $33 trillion in trading volume, with nearly 90% of that activity coming from Tether’s USDt (USDT) and Circle’s USDC, illustrating the current concentration of liquidity in a small handful of assets.

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Ripple’s foray into the stablecoin space includes RLUSD, a competitor stablecoin launched in December 2024. CoinGecko data shows RLUSD stands as the 10th-largest stablecoin by market cap, with about $1.4 billion in circulation.

Beyond stablecoins themselves, Garlinghouse highlighted Ripple’s broader push to bolster payments infrastructure through strategic acquisitions. The company bought Hidden Road, an institutional-focused prime brokerage, for $1.25 billion and GTreasury, a corporate treasury platform, for $1 billion. He said the acquisitions have helped Ripple enter a “record quarter” and that the firm has been “on a tear” since closing these deals.

Key takeaways

  • Enterprises are increasingly viewing stablecoins as a payments enabler, with senior executives pressing treasurers to outline deployment plans.
  • Global stablecoin trading volume last year exceeded $33 trillion, with about 90% concentrated in USDT and USDC, underscoring existing liquidity leadership.
  • Ripple operates RLUSD, launched in December 2024, now ranking 10th among stablecoins by market cap at roughly $1.4 billion (per CoinGecko).
  • Ripple’s acquisitions of Hidden Road ($1.25 billion) and GTreasury ($1 billion) are positioned to bolster enterprise payments and treasury management capabilities.
  • Regulatory context matters: the CLARITY Act could accelerate crypto adoption if enacted, but policymakers must avoid weaponizing policy for political ends, according to Garlinghouse.
  • Bloomberg Intelligence foresees stablecoin flows reaching $56.6 trillion by 2030, highlighting the potential scale of enterprise demand.

Stablecoins as a corporate catalyst

The conversation around stablecoins increasingly centers on real-world corporate utility. Garlinghouse framed the narrative around a critical shift: boards and CFOs are evaluating how stablecoins could streamline treasury operations, enable faster cross-border settlements, and unlock a broader set of blockchain-based services for their organizations. In this view, stablecoins are less about speculative trading and more about providing a practical, on-chain settlement layer that can integrate with existing financial workflows.

The enterprise lens also emphasizes risk management and liquidity considerations. Real-time settlements and improved cash visibility could reduce foreign exchange exposure and nested settlement delays that plague traditional cross-border payments. While these advantages exist in theory, they hinge on reliable rails, robust custody, compliance, and interoperability with conventional banking rails—a set of criteria Ripple has sought to address through its product suite and partnerships.

Ripple’s push to enterprise infrastructure

RLUSD represents Ripple’s commitment to building a native stablecoin option within its payments ecosystem. Launched in late 2024, RLUSD has quickly become a test case for how corporate users might leverage stablecoins to settle obligations on Ripple’s rails. According to CoinGecko, RLUSD ranks among stablecoins with a $1.4 billion market cap, placing it in the top tier of on-chain stablecoins by liquidity and size.

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Concurrently, Ripple’s strategic acquisitions broaden the toolkit available to enterprises. Hidden Road provides institutional-grade prime brokerage capabilities, potentially easing access to liquidity and trading infrastructure for large clients. GTreasury, a corporate treasury management platform, adds cross-functional treasury tools, enabling better visibility and control over digital-asset holdings within corporate finance operations. Garlinghouse said these acquisitions have strengthened Ripple’s trajectory, contributing to what he described as a “record quarter.”

Taken together, the RLUSD initiative and the strengthened payments backbone position Ripple to offer a more complete enterprise solution: on-chain settlement via stablecoins, coupled with governance, liquidity, and treasury management tools designed for large organizations. For investors and users watching adoption curves, the question is how quickly these capabilities translate into tangible enterprise uptake and steady revenue streams for Ripple and its partners.

Regulatory context and market outlook

The regulatory backdrop remains a pivotal variable in the trajectory of stablecoins and enterprise crypto adoption. Garlinghouse emphasized the potential impact of market-structure legislation such as the CLARITY Act, arguing that Congress could push the sector forward if crafted with clarity and sound policy. He warned against policymakers weaponizing regulation for political ends and urged a measured approach that protects the United States’ competitive standing while fostering innovation.

The broader market context underscores why this regulatory moment matters. The ongoing debate around stablecoin disclosures, reserve standards, and liquidity requirements will influence whether corporate treasuries view stablecoins as a reliable part of their long-term liquidity strategy. As policymakers weigh risk controls and consumer protections, the ability for enterprises to adopt stablecoins at scale will hinge on clear, consistent rules and interoperable infrastructure that can withstand institutional scrutiny.

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Looking ahead, the market will be watching how the CLARITY Act progresses through Congress and how Ripple, RLUSD, and related infrastructure adapt to any regulatory requirements. The combination of a strong enterprise narrative, improving payments infrastructure, and a favorable regulatory framework could accelerate corporate engagement with stablecoins, while lingering ambiguities or policy missteps could slow momentum.

Ultimately, the next phase of enterprise crypto adoption will hinge on demonstrated use cases, governance reliability, and the ability to deliver on real-world efficiency gains. For investors and builders, the key watch points are enterprise interest in RLUSD and Ripple’s broader treasury-management story, regulatory developments around stablecoins, and the degree to which large corporations actually embed stablecoins into their treasury operations and payment workflows.

As policymakers deliberate and corporates experiment, the landscape will reveal whether this era’s “ChatGPT moment” translates into durable, enterprise-grade crypto infrastructure and a measurable shift in how businesses move value across borders.

Watch for updates on CLARITY Act progress, RLUSD adoption by enterprises, and any new milestones from Ripple’s expanding payments ecosystem in the coming quarters.

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Stablecoins Will Be Crypto’s “ChatGPT Moment,” Says Ripple

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Stablecoins Will Be Crypto’s "ChatGPT Moment," Says Ripple

Ripple CEO Brad Garlinghouse said stablecoins will be the crypto sector’s “ChatGPT moment” for businesses in search of faster, more efficient payments, and that many companies are already discussing and strategizing how to implement stablecoins into their operations.

“You have boards of directors and CEOs of companies, whether it’s Fortune 500 or Fortune 2000, they’re asking their treasurers, they’re asking their CFOs, hey, what are we doing with stablecoins,” Garlinghouse told FOX Business on Friday.

“Giving the treasurer and the CFO that option is the unlock,” he said. 

Garlinghouse said this unlock would be “the ChatGPT moment of crypto” because it would be the entry point for businesses to access a broader range of blockchain-based services. 

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Garlinghouse speaking with FOX Business on Friday. Source: FOX Business

Bloomberg Intelligence predicted in early January that stablecoin flows could increase at a compounded annual growth rate of 80% to $56.6 trillion by 2030, a rise that would make stablecoins one of the most important payment tools in global finance.

Garlinghouse noted that stablecoins processed more than $33 trillion in trading volume last year, though nearly 90% of that came from Tether’s USDt (USDT) and Circle’s USDC (USDC).

Ripple launched a competitor stablecoin — Ripple USD (RLUSD) — in December 2024, which is currently the 10th largest stablecoin by market cap at $1.4 billion, CoinGecko data shows.