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. GPU.NET Ecosystem

Key Components of GPU.NET

GPU.NET’s architecture is composed of four interoperable components, each designed to tackle a specific aspect of the decentralized compute challenge. Together, they form a robust, scalable system that supports USDAI and drives the network’s mission.

1. Dapp.gpu.net: The Decentralized Compute Marketplace

  • Overview: Dapp.gpu.net is the user-facing interface of GPU.NET—a decentralized application (Dapp) that serves as a marketplace for aggregating and distributing GPU resources. It connects compute providers (data centers, individual GPU owners) with consumers (AI developers, enterprises, autonomous agents) in a seamless, permissionless environment.

  • Functionality: Users can browse available GPU resources, reserve compute capacity, and pay using USDAI or other supported assets. The Dapp leverages smart contracts to ensure transparency, enforce pricing agreements, and facilitate instant settlements.

  • Significance: By aggregating fragmented GPU resources into a single platform, Dapp.gpu.net eliminates the need for intermediaries, reduces costs, and provides real-time access to compute power tailored for AI workloads, such as model training, inference, and HPC simulations.

2. Sol.gpu.net: Tokenizing Compute on Solana

  • Overview: Sol.gpu.net is GPU.NET’s tokenization layer, built on the high-throughput Solana blockchain. It converts physical GPU compute resources into tradable real-world assets (RWAs), represented as on-chain tokens.

  • Functionality: Providers lock their GPU capacity into the network, which is then fractionalized and tokenized as compute credits. These RWAs can be traded on Solana-based decentralized exchanges (DEXs) like Raydium, enhancing liquidity and enabling market-driven pricing.

  • Significance: Sol.gpu.net bridges the physical and digital worlds, turning compute power into a fungible asset that underpins USDAI’s collateralization. Its integration with Solana ensures fast, low-cost transactions, making it ideal for scaling the compute economy.

3. Grid.gpu.net: Decentralized Compute Liquidity Management

  • Overview: Grid.gpu.net is GPU.NET’s decentralized testnet, currently operational with over 300 validators, tasked with managing compute liquidity and network stability.

  • Functionality: Validators on Grid.gpu.net monitor compute availability, validate Proof-of-Compute (PoC) transactions, and ensure that resources are allocated efficiently across the network. The testnet also serves as a proving ground for new features before the mainnet launch.

  • Significance: Grid.gpu.net ensures that GPU compute remains liquid and accessible, preventing shortages and optimizing resource distribution. Its decentralized governance model lays the groundwork for a community-driven ecosystem post-TGE.

4. GANChain: A Blockchain Optimized for AI Transactions

  • Overview: GANChain is GPU.NET’s native blockchain, a hybrid of Proof-of-Work (PoW) and Proof-of-Stake (PoS) mechanisms, custom-built to handle AI-specific transactions and multipurpose subnets.

  • Functionality: GANChain supports high-speed, secure processing of compute-related transactions, including USDAI payments, compute credit redemptions, and subnet operations for specialized AI tasks (e.g., generative adversarial networks or GANs). Its hybrid consensus balances security (PoW) with efficiency (PoS).

  • Significance: Unlike general-purpose blockchains, GANChain is tailored for AI workloads, offering low latency and high throughput. It serves as the backbone for USDAI’s interoperability and enables GPU.NET to scale beyond existing Layer 1 solutions.

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Last updated 3 months ago