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cs.DC2025
An Efficient, Reliable and Observable Collective Communication Library in Large-scale GPU Training Clusters
Mingjun Zhang, Xiaohe Hu, Menghao Zhang +21
Large-scale LLM training requires collective communication libraries to exchange data among distributed GPUs. As a company dedicated to building and operating large-scale GPU train…
cs.DC2024★ 1 cited
ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving
Tao Huang, Pengfei Chen, Kyoka Gong +5
Since the increasing popularity of large language model (LLM) backend systems, it is common and necessary to deploy stable serverless serving of LLM on multi-GPU clusters with auto…