4. Platform Business Modules

Computing Contribution and Task Distribution Module

4. Platform Business Modules

4.1 Computing Contribution and Task Distribution Module

The computing contribution module is the core entry point of the cAIToken network. All nodes---whether GPUs, CPUs, professional servers, or edge devices---must access the network through this module and register on-chain based on computing authenticity, device stability, and hardware specifications. Before node access, the platform conducts hardware proofs, computing benchmarks, and network connectivity checks to ensure nodes can submit verifiable workloads.

Task distribution on cAI is entirely aligned with mining pool reward rules---all nodes can only receive token output from the pool by completing PoW tasks. The Compute Scheduler (CS) automatically allocates base computational tasks, verification tasks, or AI training sub-tasks according to node tier, device load, historical contribution, and reputation score. All executed tasks generate verifiable proofs of work (Proof-of-Compute or Proof-of-Training), which directly serve as the basis for mining pool reward calculation.

To lower participation barriers, the platform provides visual computing access tools, portable node images, and automatic driver detection, allowing ordinary devices to contribute computing power without professional maintenance. This module establishes a complete value loop: "Provide computing → Execute task → Generate proof → Claim rewards from the pool."

4.2 AI Training Computing Rental Market

The cAIToken platform establishes a computing rental market for AI scenarios, where all computing resources come from PoW-contributing nodes and are automatically settled and scheduled through smart contracts. The core of the rental market is not direct rewards for users but rather allocation of task execution opportunities, enabling nodes to capture value via "execute training task → generate AI-PoW proof → receive pool reward."

Task initiators can submit:

Small-batch training

Inference tasks

Continuous training tasks

Data annotation and vector computation tasks

Distributed training fragments

The system automatically matches optimal node clusters based on task budget and execution requirements, improving mining pool output efficiency. All computing orders, task execution, and status verification in the market are guaranteed transparently via on-chain smart contracts.

The AI training market ensures that mining pool releases correspond to real AI workload incentives, forming a virtuous cycle: "More tasks → Higher demand → More valuable contributions."

4.3 Node and Miner Tier System

To enhance computing supply quality, the platform implements a tier system for all nodes, which directly affects task types, execution priority, and reward weight from the pool. Node tiers are not manually set but dynamically calculated by the platform based on four dimensions:

Computing performance: GPU model, memory, bandwidth, power consumption

Online stability: uptime, latency, task failure rate

Contribution history: completed tasks, training fragments, verification performance

Reputation system: malicious records, result consistency, verification scores

Higher-tier nodes are more likely to receive:

High-value training tasks

Higher verification weight

Larger share of pool rewards (still distributed according to pool rules)

Miner providers can join the cAI network via the Mining Client and automatically execute tasks for rewards. High-performance GPUs are prioritized for gradient computation in large model training, while mid-to-low devices handle inference, verification, and lightweight computation, ensuring participation opportunities for all nodes.

4.4 Stake-Boosted Computing Module

The Stake-Boost module corresponds to the pool's second release mechanism:

Nodes stake cAI to gain "task acceptance + verification rights" and earn pool rewards through verification task execution.

Staking itself does not generate tokens but grants eligibility to enter the deep task ecosystem, including:

Compute weight boost: 1.1--3x increase

Task priority: preferential access to high-reward training tasks

Validator eligibility: participate in verification tasks and earn rewards

Governance enhancement (post DAO introduction)

All rewards come from the mining pool, not additional issuance. Malicious or cheating nodes The Stake-Boost mechanism combines PoW fairness with PoS stability, giving long-term participants higher economic and governance weight.

4.5 AI Data and Training Collaboration Platform

To support the complete AI training workflow, the platform provides an AI collaboration layer, offering full-chain infrastructure from data preprocessing to on-chain result recording. All training processes must generate AI-PoW proofs to receive corresponding mining pool rewards.

The collaboration platform includes:

Data preprocessing layer: image, text, and vector data sharding and augmentation

Training pipeline: automated slicing, task dispatch, multi-node parallel training

Gradient verification mechanism: zk-proof, gradient consistency, and residual analysis

Model registry: on-chain recording of training results and weight hashes

The collaboration platform allows task initiators to leverage cAI network nodes without building their own distributed training environment. As the network scales, MPC and federated learning (FL) privacy computation will be introduced, enabling local data training while nodes continue to earn pool incentives.

4.6 Multi-Chain Computing Bridge

cAIToken establishes a cross-chain computing bridge, allowing users from different public chains and AI projects to access cAI network computing resources, while nodes earn pool rewards through cross-chain task execution.

Cross-chain capabilities include:

Cross-chain task submission (ETH/BSC/Solana/Arbitrum, etc.)

Cross-chain state and parameter synchronization (task progress, verification data, completion status)

Cross-chain incentive return (rewards automatically returned to the initiating chain)

Application layer abstraction (developers need not handle cross-chain logic)

In the future, the computing bridge will connect more AI-specific L2s, AI Rollups, and computing networks, making cAI the underlying computing layer for cross-chain AI training.