Storage sector has always been jokingly referred to as the "cold bench" in the industry, but with the recent update to Walrus Protocol v1.38.3, it seems this perception is being rewritten.

This upgrade mainly targets AI Agent application scenarios. We know that AI workflows have extremely high requirements for data reading—any slight congestion in the pathway can trigger latency issues. Walrus’s optimization this time is akin to smoothing out the entire network's "ski slopes," pushing bandwidth configurations to the limit. Considering the explosive growth expected in the AI market by 2025, this kind of infrastructure upgrade is a necessity.

The most direct improvement is reflected in data I/O efficiency. Previously, reading and writing data on the chain was as laborious as pushing objects on flat ground; this update has streamlined the process to a new level. Actual tests show that read/write speeds have increased by 70%-80% overall. Whether storing AI models or handling large files, users can feel a significant speed difference. Data uploads are almost instantaneous, with no more of the previous "lag" sensation.

It is worth noting the performance of cold storage. Traditionally, Cold Storage (cold data storage) is associated with access delays and slow responses. However, supported by the Red Stuff algorithm, Walrus enables cold data to maintain high-speed responsiveness. To put it differently—even if data is "frozen" in low-temperature storage layers, retrieval speeds can still be maintained at rocket-level rapidity.

The signal sent by this upgrade is very clear: true competitiveness lies in being able to support the next-generation application demands while others are still optimizing infrastructure. For participants holding $WAL, this performance upgrade may just be the starting point of acceleration.