DeepSeek AI News reported that last year, DeepSeek attempted to train R2 using Huawei's Ascend chips, but even with on-site assistance from Huawei engineers, the system failed to run. Issues with chip stability, cross-card communication, and software support persisted. This year, both parties have deepened their collaboration. At the end of September, DeepSeek publicly released six sets of Ascend-compatible code; in October, it was reported that Huawei is adjusting the design of its next-generation chips according to DeepSeek's model requirements.

SemiAnalysis, a semiconductor research firm, recently published real-world test results for the Ascend 950PR. Researchers selected 1,055 commonly used matrix computations from large models and found that Huawei's own low-level software enables the 950PR to achieve 86% of its theoretical peak performance—higher than NVIDIA H100's 75.5%. However, actual computation speed remains only 43% of H100's, and just 21% compared to the newer B300.

DeepSeek is reducing data movement and waiting time through software optimizations. For example, during attention computation, two previously separate data sets are now fetched together after storage format adjustments via FlashMLA. Another tool, TileLang, allows the chip to load the next batch of data while still computing. SemiAnalysis testing shows that automated scheduling reduces execution time for certain small tasks by up to about 40%. DeepSeek has also ported DeepEP, which handles MoE expert communication, but communication and computation still compete for memory bandwidth. Huawei plans further firmware improvements.

The tested 950PR primarily targets inference input processing. DeepSeek plans to use the 950DT—intended for large-scale pre-training—by year-end at the latest. The two sides have already conducted thousand-card-level tests, but achieving continuous training across tens of thousands of chips will require solving more complex coordination and stability challenges.

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