USDAI
  • Introduction
  • Why Compute-Backed?
  • Vision & Purpose
  • GPU.NET Ecosystem
    • What is GPU.NET?
    • Key Components of GPU.NET
    • GPU.NET’s Mission
    • USDAI’s Role in the Ecosystem
  • USDAI Mechanics
    • How USDAI Works
    • Pegging Mechanism
    • Collateralization
    • Issuance and Redemption
    • Stability Mechanisms
    • Why It Works
  • USDAI Architecture
    • Architecture
    • Supported Blockchains
    • Interoperability
    • Smart Contracts
    • Security Features
  • Use Cases
    • USDAI Applications
    • AI/ML Workload Payments
    • DeFi Integrations
    • Compute Reservations
    • Broader Implications
  • Acquiring USDAI
    • How to Acquire USDAI
    • Using USDAI
    • Developer Integration
  • Governance and Community
    • Governance
    • Roadmap
    • FAQ
    • Support & Community
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  1. USDAI Architecture

Supported Blockchains

USDAI’s multi-chain architecture allows it to operate on several leading blockchain networks, each chosen for its specific strengths. This approach ensures that USDAI can meet diverse use cases—ranging from DeFi integrations to high-speed AI transactions—while maintaining accessibility and efficiency.

Ethereum: DeFi Integrations and Smart Contract Flexibility

  • Purpose: Ethereum serves as USDAI’s gateway to the decentralized finance (DeFi) ecosystem, leveraging its robust smart contract capabilities and widespread adoption.

  • Functionality: On Ethereum, USDAI integrates with protocols like Aave, Compound, and Uniswap, enabling users to use it as collateral for lending, borrowing, or liquidity provision. Ethereum’s Turing-complete smart contracts support complex logic for USDAI’s minting, burning, and governance processes.

  • Advantages: Ethereum’s mature DeFi infrastructure and large user base provide USDAI with unparalleled liquidity and interoperability within the broader blockchain ecosystem.

  • Considerations: High gas fees during network congestion are mitigated by batching transactions and encouraging users to leverage Layer 2 solutions (e.g., Optimism, Arbitrum) where applicable.

Solana: High-Throughput Compute Tokenization via Sol.gpu.net

  • Purpose: Solana powers USDAI’s compute tokenization backbone through Sol.gpu.net, GPU.NET’s RWA tokenization layer, capitalizing on Solana’s high throughput and low latency.

  • Functionality: USDAI on Solana facilitates rapid minting and trading of compute-backed tokens, with transaction speeds exceeding 50,000 TPS (transactions per second). This makes it ideal for handling the high volume of compute credit tokenization and USDAI payments.

  • Advantages: Solana’s low transaction costs (often less than $0.01) and fast finality (sub-second confirmations) ensure efficient scaling for GPU.NET’s marketplace and USDAI liquidity pools on DEXs like Raydium.

  • Role in Ecosystem: Solana’s performance optimizes USDAI’s integration with real-time compute pricing and resource allocation.

Polygon: Low-Cost Transactions for Scaling USDAI Usage

  • Purpose: Polygon, a Layer 2 scaling solution for Ethereum, enables USDAI to offer low-cost transactions, making it accessible for everyday use and microtransactions.

  • Functionality: USDAI on Polygon supports cost-efficient payments for GPU compute, staking in liquidity pools (e.g., QuickSwap), and interaction with GPU.NET’s Dapp. Polygon’s compatibility with Ethereum’s EVM (Ethereum Virtual Machine) ensures seamless smart contract deployment.

  • Advantages: With gas fees often below $0.01, Polygon reduces barriers to entry for small-scale AI developers and users, while its high throughput (up to 7,000 TPS) supports USDAI’s growth as adoption scales.

  • Use Case Example: A developer paying 5 USDAI for a short ML inference task incurs minimal fees, preserving the peg’s economic value.

GANChain: GPU.NET’s Native Blockchain, Optimized for AI Transactions

  • Purpose: GANChain, GPU.NET’s custom-built blockchain, is tailored for USDAI’s primary use case—AI and HPC transaction optimization—combining the strengths of hybrid consensus mechanisms.

  • Functionality: GANChain uses a hybrid Proof-of-Work (PoW) and Proof-of-Stake (PoS) model to process USDAI payments, compute credit redemptions, and subnet-specific tasks (e.g., generative AI workloads). It supports multipurpose subnets for specialized compute needs.

  • Advantages: GANChain’s low latency and high security make it ideal for AI-native transactions, while its native integration with USDAI ensures optimal performance within the GPU.NET ecosystem.

  • Scalability: Subnets allow GANChain to scale horizontally, dedicating resources to specific workloads without congesting the main chain.

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Last updated 3 months ago