4.1 Architecture Overview: Layered Design and Modular Collaboration

Cottonia’s architecture can be divided into four core components:

(1) AI Task Layer

Responsible for the definition, orchestration, and optimization of AI tasks.

In Cottonia, AI tasks (such as model training, inference, code generation, and data labeling) are abstracted as a set of “compute demand functions” and declared using the standardized Compute Description Language (CDL).

CDL allows developers to directly specify required resource specifications (GPU type, VRAM, bandwidth, latency limits, etc.), and Cottonia automatically performs optimal matching through the underlying scheduling system.

Engineering features:

• Provides the cottonia.task SDK, supporting Python / TypeScript interfaces;

• Built-in AI Task Optimizer module that automatically selects different execution nodes based on task characteristics (I/O-intensive or compute-intensive);

• Automatically estimates Token consumption and execution time before task execution, and reduces redundant calls during model inference through intelligent sharding.

(2) Scheduling & Acceleration Layer

The technical core of Cottonia.

This layer is responsible for finding the most suitable execution path in the distributed network, similar to an “AI-native Kubernetes + GPU-aware Orchestrator.”

Core mechanisms include:

• Dynamic Compute Routing Dynamically reallocates tasks based on real-time node load, bandwidth, latency, and energy consumption models. When node network conditions fluctuate, the system triggers task migration via the CAP protocol, ensuring uninterrupted model inference.

• Gradient-Aware Acceleration For training tasks, Cottonia analyzes gradient distribution in real time and allocates training data to compute “hot zones” to reduce communication redundancy. For inference tasks, the system accelerates model response speed by 20–30% through caching optimization (Inference Cache Layer).

• Token Efficiency Optimizer A specialized optimization engine introduced for AI Coding and LLM inference scenarios.

By analyzing the model context window and call redundancy, Cottonia automatically rewrites or prunes input requests at the agent layer, reducing invalid Token transmission and computation.

Simultaneously, the system dynamically selects execution nodes with Token/Latency as dual optimization objectives.

Example: When an Agent application executes a multi-segment context code analysis task, Cottonia can merge multiple prompt rounds into parallel subtasks and reduce Token consumption by 35% through context reuse.

(3) Node & Resource Layer

This layer forms the execution backbone of Cottonia, consisting of globally distributed compute nodes and resource providers.

Nodes can be either data center-level GPU clusters or personal edge nodes (Edge Compute), registered via Cottonia’s standardized node protocol.

Main components:

• Node Kernel: Lightweight containerized runtime environment implemented in Rust, providing isolated execution, secure sandboxing, and remote verification;

• Proof-of-Performance: Records node execution via zero-knowledge proofs (ZKP) to ensure result authenticity;

• Resource Virtualization: Abstracts heterogeneous GPU/TPU resources into schedulable Compute Units (CU), supporting dynamic scaling and parallel task binding.

(4) Governance & Settlement Layer

Cottonia’s underlying settlement system is built on a hybrid chain architecture: the main chain handles task verification and settlement, while sidechains manage high-frequency calls and node reputation updates.

Through smart contracts, Cottonia supports the following mechanisms:

• Resource Settlement: Distributes rewards based on execution duration and token optimization rate;

• Reputation Governance: Adjusts node weight based on historical success rates and task latency;

• Cross-task Incentive: Encourages nodes to participate in long-term AI task execution, such as continuous code analysis and Agent orchestration.