Ascend SuperPOD

SLAI T-Rex: Full-parameter post-training of DeepSeek-V4 family on Ascend NPU SuperPOD achieves 34% MFU

Researchers present SLAI T-Rex, an end-to-end optimization framework for full-parameter post-training of trillion-parameter MoE models on Huawei Ascend NPU SuperPOD infrastructure, using the DeepSeek-V4 model family as the target workload. The system achieves 34.22% Model FLOPs Utilization, a 2.93x improvement over the open-source baseline, through hierarchical optimizations spanning model parallelism, communication orchestration, and kernel execution. Building on this infrastructure, the team develops a domain-specialized CPT and SFT pipeline for Operations Research tasks using DeepSeek-V4-Flash, producing a model that achieves 71.81% zero-shot Pass@1 on OR benchmarks, outperforming GPT-5.4-Mini by ~4 percentage points. The work is notable both as a non-GPU large-scale training system report and as a demonstration of domain specialization for complex mathematical reasoning.