POKT Network has recently published its AI Litepaper, which explores the possibilities of deploying Large Language Models (LLMs) on its protocol to provide robust and scalable AI inference services. Since its launch in 2020, POKT Network has successfully processed over 750 billion requests through its extensive network of approximately 15,000 nodes spread across 22 countries. This vast infrastructure enables POKT Network to enhance the accessibility and financialization of AI models within its ecosystem.
The AI Litepaper focuses on the alignment of incentives among various entities involved in the AI model deployment process, including model researchers (Sources), hardware operators (Suppliers), API providers (Gateways), and users (Applications). This alignment is achieved through the implementation of the Relay Mining algorithm, which creates a transparent marketplace where costs and earnings are determined based on cryptographically verified usage. By competing with centralized entities in terms of service quality, POKT Network establishes itself as a mature and permissionless network suitable for application-grade inference.
The introduction of LLMs on POKT Network enables the provision of scalable AI inference services without any downtime, leveraging the existing decentralized framework. Researchers and academics in the field of AI can now monetize their models by deploying them on the network, earning revenue based on their usage without having to manage access infrastructure or generate demand. The Relay Mining algorithm ensures transparency in the marketplace, incentivizing Suppliers to maintain a high Quality of Service.
The AI Litepaper, entitled “Decentralized AI: Permissionless LLM Inference on POKT Network,” is authored by Daniel Olshansky, Ramiro Rodríguez Colmeiro, and Bowen Li. These authors bring a wealth of expertise in various fields such as augmented reality, autonomous vehicle interaction analysis, medical image analysis, and AI/ML infrastructure development, which contributes to the comprehensive insights presented in the paper.
Daniel Olshansky has experience from working on Magic Leap’s Augmented Reality cloud and Waymo’s autonomous vehicle planning. Ramiro Rodríguez Colmeiro, who holds a PhD in signal analysis and system optimization, focuses on machine learning and medical image analysis. Bowen Li, previously an engineering manager at Apple AI/ML, led the development of Apple’s first LLM inferencing platform.
POKT Network’s AI Litepaper highlights its potential to drive innovation, adoption, and financialization of open-source models. This positions the network as a significant player in the field of permissionless LLM inference. For a more in-depth understanding of the topic, the full AI Litepaper can be accessed online.
Tags: AI