Zhipu AI’s Infra Agent Speeds Up Sovereign Hardware Clusters Using Recursive Optimization
Professor Tang Jie, founder of Zhipu AI, announced that their GLM-5.3 driven 'Infra Agent' achieved a 3.2x end-to-end throughput improvement for a production-grade inference system built on a cluster of over 100,000 domestic Chinese AI chips in under two weeks.
AsiaAI Publisher
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September 17, 2026 ·
2 min read · Source: 量子位 QbitAI · Issue #99
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This story ran in Issue #99, alongside three other stories.
AI & Machine Learning
Professor Tang Jie, founder of Zhipu AI, announced that their GLM-5.3 driven ‘Infra Agent‘ achieved a 3.2x end-to-end throughput improvement for a production-grade inference system built on a cluster of over 100,000 domestic Chinese AI chips in under two weeks. This agent autonomously identified and resolved performance bottlenecks from scratch, spanning operator precision issues, Python/C++ concurrency, and Kernel optimization.
This development from Zhipu AI, a leading Chinese LLM developer, demonstrates progress in AI’s ability to optimize its own underlying infrastructure, even on complex, large-scale domestic hardware. It highlights China’s ambition to achieve ‘recursive self-improvement’ (RSI) in AI, aiming for systems that can autonomously design and train their successors.
