ZCube, a collaborative effort by Zhipu, Yuxun Network, and Tsinghua University, has introduced a novel networking architecture to address congestion in large model inference deployments. Implemented in the GLM-5.1 coding production environment with a thousand GPUs, ZCube's architecture eliminates traditional Spine layer switches, adopting a fully flattened topology with a 2-hop network diameter. This design, coupled with a hybrid access mechanism, ensures balanced traffic load across all network switches.
Benchmark tests reveal that ZCube reduces hardware costs by 33% and boosts average GPU inference throughput by 15%, while significantly cutting the P99 first-token latency by 40.6%. These improvements highlight ZCube's potential to enhance performance and cost-efficiency in large-scale AI model deployments.
ZCube Network Architecture Enhances Large Model Inference Efficiency
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