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Zhipu Snaps Up Zhongke Jihe in Hundreds-of-Millions-Yuan Deal to Boost AI Infrastructure and Domestic Chip Compatibility

Zhipu Snaps Up Zhongke Jihe in Hundreds-of-Millions-Yuan Deal to Boost AI Infrastructure and Domestic Chip Compatibility

September 17, 2026
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Zhipu Snaps Up Zhongke Jihe in Hundreds-of-Millions-Yuan Deal to Boost AI Infrastructure and Domestic Chip Compatibility

Zhipu has reportedly invested hundreds of millions of RMB to acquire AI infrastructure firm XCore Sigma, bolstering its foundational model engineering, domestic computing power adaptation, and inference optimization.

XCore Sigma specializes in AI heterogeneous computing software infrastructure, with a focus on compilation technology. Originating from the Compilation Lab at the Institute of Computing, Chinese Academy of Sciences, its core team has contributed to compilers for domestic chips including Loongson, Sunway, Cambricon, and Huawei Ascend. The company offers full-chain capabilities—from virtual instruction set design to operator generation and optimization deployment—providing a unified software foundation for domestic AI large models.

Zhipu has previously invested in AI infrastructure companies like Jiliu Tech, Wuwen Xinqiong, and Silicon Flow, covering computing clusters and inference optimization. However, rapid growth in model call volumes has exposed infrastructure pressures. Following the March release of GLM-5, the Coding Agent’s daily call volume reached hundreds of millions, with users reporting output anomalies in complex tasks. Zhipu’s review identified engineering challenges in inference architecture and KV Cache management under high concurrency.

This acquisition is expected to enhance Zhipu’s adaptation efficiency in multi-chip environments. XCore Sigma’s compiler technology bridges different domestic chip ecosystems via an intermediate layer, improving computing power utilization. Additionally, its SigInfer inference engine, which previously claimed to reduce latency and improve throughput, could further lower model service costs if deployed in production.

As AI competition shifts from model capabilities to computing power, software infrastructure, and ecosystem building, underlying technologies like compilers are becoming a key focus for large model companies seeking long-term competitiveness.

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