iFlytek's Spark X2.5 trained on 10,000 domestic Ascend 910B GPUs with 97% uptime
机器之心 · wechat · 2026-09-11
A deep dive into how iFlytek trained Spark X2.5 (MoE, 293B-A30B) on a 10,000-GPU domestic Ascend 910B cluster, detailing the four core challenges of large-scale domestic compute:
- Compute efficiency: targeted Attention kernel optimization delivering 2x+ speedup over baseline
- Memory: sparse attention with cross-layer shared indexes plus dynamic load balancing for long sequences (+30% training efficiency)
- Communication: compression, hierarchical communication and compute parallelization (+10% efficiency)
- Stability: pre-run validation, automatic hang detection, failed-task resubmission and process-level recovery, achieving >97% effective training time
The model uses multi-teacher online policy distillation (MOPD) to boost coding and agent capabilities. In one case, Spark X2.5 autonomously explored 1,600 tool calls to optimize the DSA sparse-attention kernel, achieving 3.5x speedup over torchnpu — showing models starting to optimize the NPU software stack itself, forming a feedback loop between domestic compute and model training. Adaptation work is also underway on Cambricon MLU590 and Hygon BW1000.
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