Artificial intelligence continues to revolutionize various industries by offering advanced solutions that enhance productivity, improve decision-making, and drive innovation. Among these advancements, large language models have emerged as a critical component, capable of understanding and generating human-like text. However, the traditional approach to training these models faces significant challenges, including high computational costs, centralization risks, and scalability issues.
Scalability: By distributing the computational load across many nodes, hybrid training can handle larger datasets and more complex models without requiring immense centralized infrastructure. Cost Efficiency: Sharing computational resources among many participants can significantly reduce the overall cost of training large models, making advanced AI more accessible to smaller organizations and developers.cloud storage, to aggregate training data from multiple decentralized storage providers. Iagon Storage providers can upload large datasets to their servers and collectively agree to use this data to train the LLM on a centralized cloud like AWS.