[Submitted on 1 Apr 2026 (v1), last revised 24 Jul 2026 (this version, v2)]

Authors:Feng Ren, Ruoyu Qin, Teng Ma, Shangming Cai, Zheng Liu, Chao Lei, Dejiang Zhu, Ke Yang, Zheming Li, Jialei Cui, Weixiao Huang, Yikai Zhao, Yineng Zhang, Hao Wu, Xiang Gao, Yuhao Fu, Jinlei Jiang, Yongwei Wu, Mingxing Zhang

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Abstract:Modern GPU clusters rely on complex, heterogeneous interconnects. As large language model (LLM) serving shifts toward agentic reasoning, KVCache becomes a first-class mobile asset, driving frequent migrations and massive elephant flows that dominate the execution critical path. Operating Mooncake Transfer Engine (TE) on thousands of GPUs exposed a fundamental flaw in existing frameworks: imperative, early-binding path selection. This rigidity results in state-blind striping that ignores congestion and grey failures, leading to bandwidth stranding. This also produces operational fragility where routine faults require manual intervention.
We present TENT, a declarative orchestration engine that decouples transfer intent from physical execution. By abstracting interconnects into a unified resource pool, TENT shifts path resolution from initialization to slice-time late binding. Applications simply declare transfer intents, while TENT dynamically "sprays" fine-grained slices across rails based on real-time telemetry and predictive cost modeling. This orchestration eliminates head-of-line (HoL) blocking and enables transparent, sub-50 ms self-healing by rerouting slices around failures or degradations without application-level intervention.
TENT serves as the production data plane for LLM inference and reinforcement learning (RL) pipelines at multiple industrial clusters. Our evaluation shows that TE+ outperforms state-of-the-art baselines, including Mooncake TE, NIXL, and UCCL. In LLM inference with SGLang HiCache, TE+ achieves up to 1.36x higher throughput and 26% lower P90 time-to-first-token (TTFT) than Mooncake TE. In RL pipelines, TENT accelerates parameter updates in Moonshot Checkpoint Engine by 20-26%.

Submission history

From: Feng Ren [view email]
[v1] Wed, 1 Apr 2026 01:29:03 UTC (401 KB)
[v2] Fri, 24 Jul 2026 03:02:29 UTC (1,091 KB)