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cs.DC2025
Amoeba: Runtime Tensor Parallel Transformation for LLM Inference Services
Haoyu Chen, Xue Li, Kun Qian +3
In Large Language Model (LLM) inference services, it is challenging to make a parallelism strategy configuration, to efficiently process the requests of variance context lengths. R…
cs.DC2023★ 5 cited
Unicron: Economizing Self-Healing LLM Training at Scale
Tao He, Xue Li, Zhibin Wang +4
Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current f…