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BlockchainPOKT Network Reveals AI Litepaper on Deployment of Large Language Models

POKT Network Reveals AI Litepaper on Deployment of Large Language Models

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POKT Network recently released its AI Litepaper, which delves into the utilization of Large Language Models (LLMs) on its protocol to offer reliable and scalable AI inference services. Since its launch on the Mainnet in 2020, POKT Network has successfully processed over 750 billion requests through a network comprising approximately 15,000 nodes across 22 countries. This extensive infrastructure places POKT Network in a prime position to enhance the accessibility and financialization of AI models within its ecosystem.

The AI Litepaper sheds light on the alignment of incentives among various entities, including model researchers (Sources), hardware operators (Suppliers), API providers (Gateways), and users (Applications), through the implementation of the Relay Mining algorithm. This algorithm creates a transparent marketplace where costs and earnings are determined based on cryptographically verified usage. As a result, the protocol’s quality of service competes with centralized entities, establishing it as a mature permissionless network for application-grade inference.

Entitled “Decentralized AI: Permissionless LLM Inference on POKT Network,” the AI Litepaper introduces the potential integration of LLMs on the network to provide robust and scalable AI inference services. By leveraging the existing decentralized framework, AI researchers and academics can monetize their models by deploying them on the network, earning revenue based on usage without the need to manage access infrastructure or generate demand. The Relay Mining algorithm ensures a transparent marketplace and incentivizes Suppliers to maintain a high quality of service.

The AI Litepaper was authored by Daniel Olshansky, Ramiro Rodríguez Colmeiro, and Bowen Li, who bring a wealth of expertise in augmented reality, autonomous vehicle interaction analysis, medical image analysis, and AI/ML infrastructure development. Their contributions to the paper provide comprehensive insights into the field.

Daniel Olshansky has experience from working on Magic Leap’s Augmented Reality cloud and Waymo’s autonomous vehicle planning. Ramiro Rodríguez Colmeiro, a PhD in signal analysis and system optimization, focuses on machine learning and medical image analysis. Bowen Li, formerly an engineering manager at Apple AI/ML, led the development of Apple’s first LLM inferencing platform.

POKT Network’s AI Litepaper emphasizes its potential to drive innovation, adoption, and financialization of open-source models, positioning the network as a key player in permissionless LLM inference. For a more in-depth understanding, the full AI Litepaper can be accessed online.

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