Post-training then shapes behavior through instruction-following, safety alignment, and task-specific data. Completing it on Ascend silicon is a genuine result for the platform, but it doesn’t demonstrate that the chips can pre-train a frontier model from scratch, which is the heavier and costlier job.
DeepSeek launches 1.6 trillion parameter V4 on Huawei chips as U.S. escalates AI theft accusations
Huawei unveils new "Atlas 350" AI accelerator with 1.56 PFLOPS of FP4 compute & up to 112 GB of HBM
Back in August, it was reported that DeepSeek couldn’t complete a single successful training run for its R2 model in Ascend chips, even with Huawei engineers on site, blaming unstable performance, slow chip-to-chip interconnects, and gaps in Huawei's CANN software stack, its substitute for Nvidia's CUDA. The company fell back on Nvidia GPUs for training and left Ascend on inference. DeepSeek-V4-Pro , released in April, was the first DeepSeek model built around Ascend from the outset.
As for the claim coming out of Shenzen, it carries no benchmarks , gives no figure for how long the run took, how it compared to the same job on Nvidia hardware, or how efficiently the 1,000-chip cluster was used. It’s ultimately just another addition to a series of dubious claims that have come from the Chinese state without anything to back them up; DeepSeek itself hasn’t commented.
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Key considerations
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Reference reading
- https://www.tomshardware.com/tech-industry/artificial-intelligence/SPONSORED_LINK_URL
- https://www.tomshardware.com/tech-industry/artificial-intelligence/huawei-led-team-claims-it-post-trained-deepseeks-1-6-trillion-parameter-models-on-ascend-910c-chips#main
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