3.2.3 AI Computing Power Market

Building a "Decentralized Intelligent Power Pool"

Functions such as the neural intelligent contract and AI security oracle of Aegis AI Core (Imperial AI Engine) require massive computing power support. The AI computing power market of DePIN, by integrating the idle computing power of nodes around the world, has created a computing power supply network with "low cost and high privacy":

• Computing power aggregation: Making "idle resources" generate valueNode operators can independently choose to connect part or all of their computing power to the AI computing power pool. The system realizes efficient scheduling of computing power through "computing power standardization" technology (unifying the computing power of devices of different brands and models into "standard computing power units"):

• Individual users: Deploying a node equipped with an RTX 4090 graphics card can provide about 10 TFLOPS (trillions of floating-point operations per second) of computing power, and can obtain 50-80 AIS rewards per month.

• Institutional users: Deploying a server cluster (such as 10 servers equipped with A100 graphics cards) can provide more than 1 PFlops of computing power, becoming a "computing power tycoon" and giving priority to undertaking complex tasks of Aegis AI Core (such as smart contract auditing and anonymous credit evaluation model training), with an annualized return of 20%-30%.

• Privacy protection: Parallel "computing power contribution" and "data isolation"Different from traditional centralized computing power platforms (such as AWS SageMaker), the AI computing power market of DePIN not only provides computing power, but also strictly protects user data privacy:

  • Federated learning framework: During AI model training, data does not need to be uploaded to the central server, and only the model parameter update results are transmitted to the computing power pool through an encrypted channel. Nodes cannot access the original data.

  • Anonymization of computing power tasks: The AI tasks (such as "optimizing the anonymous credit scoring model") undertaken by nodes only display the task number and computing power requirements, and do not include information such as the identity of the task initiator and specific application scenarios, avoiding "abuse of computing power".