SLAI T-Rex – Full-parameter post-training of DeepSeek-V4 on Ascend SuperPOD
WHY IT MATTERS
A paper details the full-parameter post-training of DeepSeek-V4 on an Ascend SuperPOD cluster, demonstrating large-scale training on Huawei's AI hardware. The work achieved 31 upvotes on Hugging Face and represents progress in non-NVIDIA AI infrastructure.
A paper from SLAI describes full-parameter post-training of DeepSeek-V4 on an Ascend SuperPOD cluster, achieving 31 upvotes on Hugging Face. This is a documented run of a large model on Huawei’s AI hardware.
For operators facing GPU allocation delays or regulatory restrictions, this provides a validated recipe for training on Ascend—reducing the dependency risk on NVIDIA toolchains. The second-order effect is that procurement decisions may shift toward multi-vendor compute planning, especially for post-training phases where precision and orchestration matter as much as raw throughput. Builders can now benchmark their own workflow portability against a known reference, lowering the cost of integrating alternative accelerators. The immediate operational change: teams should audit their training stack for Ascend compatibility, as the barrier to deploying on non-NVIDIA hardware just became lower.
SOURCE
HuggingFace
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