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Market News

Avalon Labs Launches AI-Powered RWA Marketplace on BNB Chain

New whitepaper introduces Commercial Rights Tokenization and AI-Model-as-a-Service.

Written By Thales Rodrigues Thales Rodrigues
Fact Checked by Jahnu Jagtap Jahnu Jagtap
Published 2025-10-30·Updated 8 months ago
Make The Crypto Times preferred on GoogleGoogle
Last updated: October 31, 2025 10:57 AM
Published 2025-10-30
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Last updated: October 31, 2025 10:57 AM
Published 2025-10-30
Avalon Labs Launches AI-Powered RWA Marketplace on BNB Chain

Avalon Labs has unveiled its whitepaper for an AI-backed real-world asset (RWA) marketplace and AI-Model-as-a-Service (AI-MaaS) platform, now live on BNB Chain. The launch combines artificial intelligence (AI) with blockchain infrastructure, creating a system where GPU owners and AI developers can collaborate through tokenized compute access and commercial rights.

The initiative introduces a new standard, Commercial Rights Tokenization (CRT), allowing businesses to tokenize enforceable commercial rights tied to goods, commodities, and services. 

Today, we’re excited to announce the official release of our Whitepaper for the industry’s first on chain AI-backed RWA marketplace and AI-Model-as-a-Service (AI-MaaS). The exclusive initial launch on @BNBCHAIN!

This marketplace is open to all GPU hardware owners and AI model… pic.twitter.com/1StYiyToN4

— Avalon Labs (@avalonfinance_) October 30, 2025

Unlike standard RWA securitization, CRT targets access and service rights, turning everyday commercial contracts into enforceable on-chain transactions.

Avalon’s RWA marketplace enables enterprises, traders, and asset managers to tokenize commercial rights linked to tangible goods and services, bringing operational assets on-chain with legal enforceability. 

The system lets businesses unlock liquidity from existing contracts, while investors access yield-bearing real-world assets through transparent, programmable tokens.

Allocation model for AI compute

According to Avalon’s report, the marketplace operates under a Structured Allocation model, dividing AI compute access into two tiers:

  • Senior Allocation (sAI): Prioritized compute slots for consistent availability.
  • Junior Allocation (jAI): Flexible, market-driven access tied to demand.

This tiered framework aims to balance resource volatility while applying DeFi-style transparency to enterprise-level AI usage.

Avalon’s marketplace also integrates reinforcement learning models built on H200 GPU infrastructure, offering plug-and-play AI systems for developers and institutions without requiring in-house model training or hardware management.

From token burn to new AI and RWA strategy

Following Avalon’s September news, when the firm burned $1.88 million worth of AVL tokens, reducing its circulating supply by 37% since June, the move tied protocol revenue and token supply to ecosystem growth.

Together, the token burn and whitepaper release mark Avalon’s step from balance-sheet focus to structural experimentation, linking AI compute, tokenized assets, and blockchain finance in a single frame.

Also read: Avalon Labs Launches Bitcoin-Backed USDa Stablecoin

Disclaimer: The information researched and reported by The Crypto Times is for informational purposes only and is not a substitute for professional financial advice. Investing in crypto assets involves significant risk due to market volatility. Always Do Your Own Research (DYOR) and consult with a qualified Financial Advisor before making any investment decisions.

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TAGGED:Artificial Intelligence (AI)BinanceRWA
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Thales Rodrigues- Crypto Journalist
By Thales Rodrigues
Follow:
Thales is a Brazilian economist passionate about marketing, bringing with him experience from the country’s largest banks and financial institutions. Outside of work, he dedicates his time to sports, family, and business studies.
Jahnu Jagtap
By Jahnu Jagtap
Follow:
Jahnu Jagtap is a Senior Crypto Research Analyst at The Crypto Times, based in Ahmedabad, India. He leads the publication's technical research desk, tracking daily market momentum, Ethereum network realized profits, institutional capital flows (such as ETF inputs and major fund performance), and SEC tokenization frameworks. All advanced on-chain analysis and macro-policy developments pass through his desk to guarantee empirical precision before publication. Jahnu holds professional certifications in Blockchain and Its Applications from SWAYAM MHRD and Cryptocurrency from Upskillist. His deep immersion in live blockchain data and quantitative market cycles has shaped his meticulous approach to technical verification and structural editing on multi-layered macro stories.

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