In-Depth Analysis: Why Does Zhipu Implement RSI at the Infra Layer?

Zhipu recently focused on Recursive Self-Improvement (RSI) at the infrastructure layer, building an inference system powered by GLM-5.3. This Infra system directly supports the efficient operation of the GLM-5.3-Flash model, enabling it to process over 62 trillion tokens within just six days of its release, quickly becoming one of the most widely used models on the internet.

The core significance of this move lies in the optimization of computing power costs and inference efficiency. With the exponential growth in the processing scale of large models, computing power costs have become a key bottleneck constraining development. Implementing RSI at the Infra layer means the system can continuously improve its own operational efficiency through recursive optimization, thereby seeking breakthroughs in the massive computing power consumption battle.

Furthermore, the system runs entirely on 100,000 domestic AI chips, providing an important validation case for the autonomy and controllability of the domestic computing power supply chain. It demonstrates that domestic computing chips already possess the capability to support ultra-large-scale, high-concurrency inference services for large models, helping to alleviate the industry's sole reliance on overseas computing hardware.

In terms of industry impact, Zhipu's practice provides a new perspective for the technological roadmaps of large model companies. In the future, the deep integration of algorithms and underlying infrastructure will become the norm. Teams with in-house Infra optimization capabilities will occupy a more favorable position in the commercialization of large models and computing power cost control.

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