High storage costs and easily deleted data—these pain points have troubled many content platforms and AI companies. The emergence of the Walrus project offers an interesting solution to these issues.

Instead of handing all files over to a centralized cloud service provider, Walrus has chosen a different path: distributing storage tasks across numerous nodes in the network through a distributed architecture to reduce costs and risks. The cleverness of this design lies in simplifying the concept of "large file storage."

On the technical level, Walrus adopts an innovative coding scheme (officially called Red Stuff, with related papers already published). The core logic of this scheme is straightforward: dividing a large file into many small segments and dispersing them across different nodes. Even if some nodes lose data, the system can quickly restore the complete file through redundancy coding, without needing to store multiple full backups. The result is savings in storage space and reduced network transmission costs. For video distribution platforms, AI model weight libraries, or businesses requiring global distributed deployment, this is a very friendly solution.

What role does the token WAL play in this ecosystem? Simply put, it acts as a "pass." Users pay with WAL to store data; node operators providing hardware and bandwidth earn WAL as rewards; various parameter adjustments for ecosystem governance are also conducted through token voting. The official design aims to make WAL both capable of reliably covering storage costs and ensuring node long-term data availability through incentive mechanisms.

Who will actually use this system? Content platforms need to provide efficient video distribution services; AI companies need to store massive training datasets; cross-border e-commerce platforms require global redundant backups—these scenarios make Walrus a strong supplement to traditional cloud storage, or a "decentralized backup solution."