AI model training data storage has always been a core bottleneck for Web3 applications. Recently, the Walrus Protocol launched the Encoding V2 solution, which provides the answer—this system is designed for large-scale training data, directly solving the storage efficiency problem.

In terms of actual performance, V2 has indeed made a qualitative leap compared to the previous generation. Storage and read/write efficiency increased by 200%, data compression ratio reached 1:15, a level rarely seen in the industry. In real-world applications, storage costs have decreased by 65%, and retrieval speed has tripled. Three leading AI companies have already chosen to integrate, and billions of training data are gradually migrating to this ecosystem.

Interestingly, the token design of this protocol creates a natural demand cycle. All data storage and retrieval interactions are settled with the ecosystem token, creating real usage demand. From the market performance, large buyers are already positioning related assets, and institutional participation is very active.

The combination of AI and blockchain has always been seen as the next growth engine, and breakthroughs in the data storage layer could accelerate this process. When storage costs and speeds are no longer bottlenecks, the application layer’s imagination space will significantly expand.