Decentralized AI: Permissionless LLM Inference on POKT Network (2405.20450v1)

Abstract: POKT Network's decentralized Remote Procedure Call (RPC) infrastructure, surpassing 740 billion requests since launching on MainNet in 2020, is well-positioned to extend into providing AI inference services with minimal design or implementation modifications. This litepaper illustrates how the network's open-source and permissionless design aligns incentives among model researchers, hardware operators, API providers and users whom we term model Sources, Suppliers, Gateways and Applications respectively. Through its Relay Mining algorithm, POKT creates a transparent marketplace where costs and earnings directly reflect cryptographically verified usage. This decentralized framework offers large model AI researchers a new avenue to disseminate their work and generate revenue without the complexities of maintaining infrastructure or building end-user products. Supply scales naturally with demand, as evidenced in recent years and the protocol's free market dynamics. POKT Gateways facilitate network growth, evolution, adoption, and quality by acting as application-facing load balancers, providing value-added features without managing LLM nodes directly. This vertically decoupled network, battle tested over several years, is set up to accelerate the adoption, operation, innovation and financialization of open-source models. It is the first mature permissionless network whose quality of service competes with centralized entities set up to provide application grade inference.

• The paper demonstrates that POKT Network enables decentralized, permissionless LLM inference by leveraging its established RPC infrastructure.
• It details a Relay Mining algorithm and incentive model that align stakeholders to ensure transparent and verifiable service provision.
• The work highlights improved experimental flexibility and cost-effective model deployment, lowering barriers for AI research and open-source development.

Decentralized AI: Permissionless LLM Inference on POKT Network

The presented paper provides a comprehensive examination of the POKT Network's capabilities and potential in delivering decentralized AI inference services. The core proposition is to leverage the POKT Network's existing decentralized Remote Procedure Call (RPC) infrastructure to facilitate LLM inference without the constraints associated with centralization.

Overview of POKT Network

The POKT Network, which has been operational since 2020, is distinguished by its decentralized and permissionless approach to RPC services across multiple blockchains. The network employs a Relay Mining algorithm as a key component to ensure transparent and verifiable service provision. This setup has allowed the POKT Network to handle over 740 billion requests, underscoring its robustness and scalability.

Core Problem Addressed

The paper identifies several infrastructural challenges within the current AI landscape:

1. Restricted Model Experimentation: The high resource requirements for AI infrastructure limit the experimentation with different models. Delegating infrastructure requirements to API providers often results in a restricted range of models supported, hindering research and development.
2. Unsustainable Model for Open Source: Despite the innovation it brings, open-source AI development struggles with monetization. The reliance on third-party support diminishes incentives for long-term innovation.
3. Inequality in Market Access: Centralized infrastructure providers prioritize enterprise customers, thus reducing the availability of cost-effective options for small and medium-sized enterprises.

Unique Value Proposition of POKT Network

The POKT Network aims to remediate these issues through its decentralized architecture, which aligns economic incentives among various network participants (Model Sources, Suppliers, Gateways, and Applications). This structure allows for:

• Proven Network Reliability: Utilizing an established decentralized RPC infrastructure to support model inference.
• Separation of Concerns: Each participant in the ecosystem focuses on specific roles, thus enhancing overall efficiency.
• Incentive Alignment: Economic incentives are aligned through cryptographic metering and performance measurements, promoting transparency and competition.
• Permissionless Model Support: The network's open access nature encourages the introduction of new models, providing a broad marketplace for AI services.

Decentralized AI Inference Stakeholders

The decentralization of AI inference involves various actors:

• Model Providers (Gateways and Watchers): Gateways facilitate high-level services over the POKT infrastructure, while Watchers discreetly monitor service quality.
• Model Users (Applications): Applications can access the network either directly or through Gateways, offering flexibility in privacy and usage.
• Model Suppliers (Hardware Operators): Suppliers run inference nodes and are rewarded based on their service quality and usage.
• Model Sources (AI/ML Researchers): Researchers can publish models on the network and earn revenue without managing the infrastructure.

Implications and Future Directions

Theoretical Implications

The proposed decentralized model could inspire similar approaches in other domains requiring heavy computational resources. By democratizing access to AI inference capabilities, the POKT Network sets a precedent for future decentralized computational platforms.

Practical Implications

Practically, this model could lower the barrier to entry for AI research and applications, enabling smaller entities to experiment and innovate without significant upfront infrastructure investments. The monetization framework presents a sustainable business model for open-source AI developers.

Future Research Directions

The intersection of decentralized infrastructure and AI opens numerous avenues for further research:

• Tokenomics: Further work is needed to refine the economic incentives to ensure the long-term sustainability of the network.
• Trusted Execution Environments (TEE): Exploring secure computation methods can enhance trust in a decentralized setup.
• Model Inference Verification: Techniques to verify the integrity and performance of models in a permissionless network.
• Adversarial Resilience: Strategies to mitigate and manage adversarial behavior within the network.

Conclusion

The POKT Network's extension into AI inference services presents a compelling proposition for the decentralized provision of computation-intensive tasks. By leveraging existing infrastructure and aligning stakeholder incentives, this approach could facilitate a more open and equitable AI landscape. The ongoing research and development efforts are poised to address the remaining challenges, potentially setting the stage for broader adoption and innovation in decentralized AI ecosystems.