SLAI T-Rex框架优化Ascend SuperPOD上的DeepSeek-V4模型

报道来源 [3]

1. arXiv cs.AI TIER_1 English(EN) · Dongfang Li, Xiaodong Luo, Ruoyu Sun, Xuhui Chen, Linyuan Qiu, Jian Meng, Zhengxuan Lu, Yiting Wang, Yucheng Xie, Tao Guo, Tianxiang Fang, Jing Li, Sihang Chen, Shihao Hong, Chang Liu, Weihua Dai, Zirong Zeng, Ziwei Zhu, Zhuohan Wang, Zhengjun Yue, Igor … · 2026-07-23 04:00

   SLAI T-Rex：在Ascend SuperPOD上对DeepSeek-V4系列进行全参数后训练

   arXiv:2607.20145v1 Announce Type: cross Abstract: Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and …

2. Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-22 00:00

   SLAI T-Rex：在 Ascend SuperPOD 上对 DeepSeek-V4 系列进行全参数后训练

   Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution. While most large-sca…

3. r/LocalLLaMA TIER_1 English(EN) · /u/pmttyji · 2026-07-23 08:13

   [Paper] SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

   <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1v47kqc/paper_slai_trex_fullparameter_posttraining_of_the/"> <img alt="[Paper] SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD" src="https://preview.redd.it/3bpuadlysxeh1.…