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Decentralized AI: Permissionless LLM Inference on

· 5 min read
Dora Noda, known for its Remote Procedure Call (RPC) infrastructure, is expanding into AI inference services. This evolution leverages its open-source, permissionless design to create a marketplace where model researchers, hardware operators, API providers, and users interact seamlessly. The network's Relay Mining algorithm ensures a transparent and verifiable service, presenting a unique opportunity for large model AI researchers to monetize their work without infrastructure maintenance.

The Core Problem

The AI landscape faces significant challenges, including:

  • Restricted Model-Serving Environments: Resource-intensive infrastructure limits AI researchers' ability to experiment with various models.
  • Unsustainable Business Models for Open Source Innovation: Independent engineers struggle to monetize their work, relying on major infrastructure providers.
  • Unequal Market Access: Enterprise-grade models dominate, leaving mid-tier models and users underserved.’s Unique Value Proposition addresses these issues by decoupling the infrastructure layer from the product and services layer, ensuring an open and decentralized framework. This setup enables high-quality service delivery and aligns incentives among all network participants.

Key benefits include:

  • Established Network: Utilizing an existing network of's services to streamline model access and service quality.
  • Separation of Concerns: Each stakeholder focuses on their strengths, improving overall ecosystem efficiency.
  • Incentive Alignment: Cryptographic proofs and performance measurements drive competition and transparency.
  • Permissionless Models & Supply: An open marketplace for cost-effective hardware supply.

Decentralized AI Inference Stakeholders

Model Providers: Coordinators

Coordinators manage the product and services layer, optimizing service quality and providing seamless access for applications. Coordinators discreetly ensure supplier integrity by posing as regular users, offering unbiased performance assessments.

Model Users: Applications

Applications typically use first-party coordinators but can also access the network with a third-party for enhanced privacy and cost savings. Direct access allows for diverse use case experimentation and eliminates intermediary costs.

Model Suppliers: Hardware Operators

Suppliers run inference nodes to earn tokens. Their competencies in DevOps, hardware maintenance, and logging are crucial for network growth. The permissionless approach encourages participation from various hardware providers, including those with idle or dormant resources.

Model Sources: Engineers & Researchers

Researchers and institutions that open-source models can earn revenue based on usage. This model incentivizes innovation without the need for infrastructure maintenance, providing a sustainable business model for open-source contributors.

Working with Cuckoo Network collaborates with Cuckoo Network to revolutionize AI inference through a decentralized and permissionless infrastructure. This partnership focuses on leveraging both platforms' strengths to create a seamless and efficient ecosystem for AI model deployment and monetization.

Key Collaboration Areas

  • Infrastructure Integration: Combining's robust RPC infrastructure with Cuckoo Network's decentralized model-serving capabilities to offer a scalable and resilient AI inference service.
  • Model Distribution: Facilitating the distribution of open-source AI models across the network, enabling researchers to reach a broader audience and monetize their innovations without the need for extensive infrastructure.
  • Quality Assurance: Implementing mechanisms for continuous monitoring and assessment of model performance and supplier integrity, ensuring high-quality service delivery and reliability.
  • Economic Incentives: Aligning economic incentives across all stakeholders through cryptographic proofs and performance-based rewards, fostering a competitive and transparent marketplace.
  • Privacy and Security: Enhancing privacy-preserving operations and secure model inference through advanced technologies like Trusted Execution Environments (TEE) and decentralized data storage solutions.
  • Community and Support: Building a supportive community for AI researchers and developers, providing resources, guidance, and incentives to drive innovation and adoption within the decentralized AI ecosystem.

By partnering with Cuckoo Network, aims to create a holistic and decentralized approach to AI inference, empowering researchers, developers, and users with a robust, transparent, and efficient platform for AI model deployment and utilization. You can now try decentralized text-to-image API at

Input/Output of a Decentralized Inference Network

LLM Inputs to Cuckoo Network:

  • Open-source models
  • Demand from end-users or Applications
  • Aggregated supply from commodity hardware
  • Quality of service guarantees

LLM Outputs from Cuckoo Network:

  • No downtime
  • Seamless model experimentation
  • Public model evaluation
  • Privacy-preserving operations
  • Censorship-free models

Web3 Ecosystem Integrations's RPC protocol can integrate with other Web3 protocols to enhance Decentralized AI (DecAI):

Data & Storage Networks: Seamless integration with decentralized storage solutions like Filecoin/IPFS and Arweave for model storage and data integrity.

Compute Networks: Complementary services leveraging decentralized computing layers like Akash and Render, supporting both dedicated and idle hardware.

Inference Networks: Flexible deployment models and robust ecosystems supporting diverse inference tasks.

Applications: AI agents, consumer apps, and IoT devices benefit from DecAI inference for personalized services, data privacy, and edge decision-making.

Summary's established infrastructure and economic design unlock new opportunities for open-source AI. By providing a decentralized and verifiable service, it bridges the gap between open-source AI and Web3, enabling innovative, sustainable, and reliable services. This approach allows for greater model diversity, better market access for SMEs, and a new business model for open-source researchers. Future developments will continue to expand the ecosystem, ensuring remains a robust and adaptable solution in the evolving AI and blockchain landscapes.