Page 2: Architecture and Technical Overview
Cortensor is built on a multi-layered architecture designed to ensure scalability, security, and efficient AI inference across a decentralized network. This section provides an overview of the key components of the Cortensor platform and the technologies that drive its functionality.
Core Components
Multi-Layer Blockchain Architecture
Layer 1 (L1): Registration and Onboarding
Technology: Ethereum or Layer 2 solutions like Arbitrum, Base, or Optimism.
Purpose: This layer handles the secure registration and onboarding of miners and users, forming the foundation of the Cortensor network. It ensures that all participants are verified and trusted, laying the groundwork for a secure and reliable network.
Layer 2 (L2): Health and Capability Verification
Technology: Layer 2 chains optimized for scalability and speed.
Purpose: This layer monitors the health and capabilities of nodes through Proof of Inference (PoI) and Proof of Useful Work (PoUW) mechanisms. It ensures that all nodes meet the required standards for participation and that the network remains robust and reliable.
Layer 3 (L3): User Interaction and Service Layer
Technology: Layer 2 or Layer 3 chains tailored for dApp interactions and user services.
Purpose: This layer facilitates interactions between users and the Cortensor network, allowing them to access AI inference services and other functionalities. It is the primary interface for users to interact with Cortensor’s decentralized AI capabilities.
Proof of Inference (PoI) and Proof of Useful Work (PoUW)
Proof of Inference (PoI): Ensures that AI inference tasks are performed correctly and consistently across different nodes using the same model. This mechanism validates the accuracy of the inference results by comparing outputs from multiple nodes and ensuring they match within an acceptable range.
Proof of Useful Work (PoUW): Goes beyond simple validation by ensuring that the work performed by nodes is not only correct but also useful and relevant. For example, nodes might generate synthetic data or validate the outputs of AI models by cross-referencing with other nodes. This process ensures that the network’s resources are used effectively and that the outputs generated are meaningful.
Node Lifecycle and Task Assignment
Node Activation: When a node joins the network, it signals its readiness by activating itself. The network then assigns tasks to the node, such as PoI and PoUW tasks, to verify its capabilities and ensure it meets the network’s standards.
Task Execution: Nodes are tasked with creating virtual blocks or transactions, generating detailed prompts based on agreed topics, and providing inference results. These tasks are assigned based on the node's capabilities and performance, with more complex tasks reserved for higher-performing nodes.
Ephemeral Nodes: Once a node has successfully completed a series of tasks, it enters an "ephemeral" state, where it can serve user requests for a limited time. These nodes can be reserved or public, depending on the user’s requirements for privacy, performance, and other factors.
User Sessions and Resource Allocation: Users create sessions by depositing tokens, which are used to allocate network resources for AI inference tasks. The network uses these sessions to plan capacity and ensure that nodes are available to meet user demands.
Quantization and Model Support
LLM Quantization: Cortensor utilizes quantization to support a wide range of hardware, from low-end CPUs to high-end GPUs. Quantization reduces the precision of the models, allowing them to run on less powerful hardware without sacrificing too much performance. This approach ensures that Cortensor can accommodate a broad spectrum of devices, making AI inference more accessible and cost-effective.
Model Flexibility: Initially, Cortensor supports quantized models, particularly for tasks that do not require high precision. Over time, the platform will expand to support higher bit quantization and non-quantized models, enabling more complex and resource-intensive AI tasks.
Data Management and Privacy
Off-Chain Data Storage: Cortensor uses decentralized storage solutions like IPFS to manage large volumes of data off-chain. This approach reduces the cost of operation and ensures that data remains accessible and secure.
Data Encryption: All data transmitted between nodes is encrypted, ensuring privacy and security. Users can choose to use public or private nodes depending on their privacy requirements, with the option to select higher-security nodes for sensitive tasks.
Conclusion
Cortensor’s architecture is designed to provide a flexible, scalable, and secure platform for decentralized AI inference. By leveraging a multi-layered blockchain structure, innovative proof mechanisms, and advanced quantization techniques, Cortensor ensures that AI resources are accessible to all, regardless of their hardware capabilities. This architecture not only supports the current needs of AI inference but also lays the foundation for future advancements in decentralized AI technologies.
Reference: https://docs.cortensor.network/technical-architecture
Disclaimer: This page and the associated documents are currently a work in progress. The information provided may not be up to date and is subject to change at any time.
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