Model Training

• Distributed Computing Network:

Users can stake tokens to access Hurley-AI's decentralized computing network (e.g., a blockchain-based GPU/CPU resource-sharing platform), contributing idle computing power to support AI model training or inference tasks.

• Pay-as-You-Go:

Developers use tokens to pay for computing power, supporting containerized task deployments for mainstream AI frameworks (e.g., PyTorch, TensorFlow). Costs are dynamically priced based on task complexity (e.g., training duration, model parameter size).