GLM-5.3 Optimization System Achieves 3.2x Throughput Improvement

The GLM-5.3-driven Infra Agent has participated in optimizing the inference system of GLM-5.3-Flash, achieving an end-to-end throughput improvement of 3.2 times compared to the initial performance. Tang Jie, co-founder and chief scientist of Zhipu, pointed out that the Agent often encounters difficulties in troubleshooting. While it can identify modifications that lead to a 20% decrease in throughput, it cannot determine the specific level of the problem. To address this, Zhipu developed a set of tools to help the Agent autonomously check the results, time consumption, and efficiency of code modifications. Through this method, the Agent identified issues such as long context computation errors, KV transmission blocking, and repeated calculations in the decoding kernel, with one kernel achieving a speedup of 1.71 times after being rewritten. Tang believes that the role of engineers will shift towards designing feedback, responsible for setting goals and reviewing high-risk modifications, while the Agent will handle hypotheses, code modifications, and experiments. Although there is still a distance to a complete RSI, a minimal closed loop has initially formed, reflecting the concept of "model optimization system, system serving the model."

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