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ZCube is a new network architecture that flattens the topology and mixes single/multi-rail access to optimize KV Cache transmission in long-context and PD separation scenarios. In the GLM-5.1 production cluster, it achieved a 33% reduction in switch/optical module costs, a 15% increase in GPU inference throughput, and a 40.6% decrease in TTFT P99.
This paper presents ZCube, a novel network architecture developed by Z.ai, Harnets.AI, and Tsinghua University to address topology-induced congestion in Prefill-Decode disaggregated LLM inference clusters. Production deployments on GLM-5.1 coding workloads achieved a 33% reduction in network CapEx, 15% throughput improvement, and 40.6% reduction in TTFT P99 latency.