深度解读：智谱为什么要在 Infra 层搞 RSI？

TL;DR - Zhipu says it used a GLM-5.3-powered infrastructure agent and dense feedback loops to deploy GLM-5.3-Flash across 100,000 domestic AI chips, raising end-to-end throughput 3.2×. The effort illustrates its strategy of using AI-driven systems optimization to overcome hardware constraints and reduce inference costs.

• The system combines tensor parallelism, layer partitioning, ReplaySSM recomputation, W8A8 quantization, mixed-precision KV-cache compression, and decoupled encoding, prefill, and decoding.
• Its “dense feedback” framework gives the agent correctness, system-behavior, and performance signals for diagnosing numerical drift, communication bottlenecks, and redundant kernel computation.
• Zhipu reports moving full production traffic to the cluster within two weeks; GLM-5.3-Flash then processed more than 62 trillion tokens in six days.
• The company is investing heavily in inference infrastructure because surging coding-model demand made compute capacity its primary constraint on product availability and revenue.