Can China train frontier models on its own chips alone?

What came out together in one week?

This week's news on Chinese AI infrastructure pointed in one direction. Figures showing that clusters built with domestic accelerators have entered commercial operation came out in the same week as a report that training, too, will move to domestic chips.

On November 3, Pandaily reported that Huawei's Ascend 950 SuperPoD won an award at the World Internet Conference (WIC) and that Huawei said SuperPoD deployments had passed 1,000 sets source. On the same day, Pandaily reported that Moore Threads is commercially operating a cluster of 2,112 MTT S5000 GPUs in Wuxi, Jiangsu Province source. Also on the same day, a Chinese media article saying DeepSeek plans to pretrain V5 on Ascend was reposted to Reddit r/LocalLLaMA source. A day earlier, on November 2, came news of an acquisition involving EDA (electronic design automation, chip design software) company Empyrean source and an analysis note on China's Ministry of Commerce Announcement No. 48 source. On October 31, CCID, a government-affiliated research institute, released a report on inference demand sourcesource.

Taken one by one, these are corporate announcements, an acquisition and policy news. Taken together, they can be read as signals appearing in the same week across multiple layers of the supply chain: design tools, cluster operations, model training and demand. The question for readers is just one: do these signals show not only that "inference works" but also that "training works"?

Are 45% and 92.9% the same kind of number?

Huawei said its Ascend 950 SuperPoD, which links 1,024 NPUs (neural processing units), achieves 45% utilization in training source. Moore Threads said its Wuxi cluster has an operating rate of 92.9% and 55 customers source. Both figures come from the companies themselves.

On the numbers alone, Moore Threads looks twice as high, but the two metrics can be seen as measuring different things. Training utilization usually means the share of a chip's maximum compute actually used for training, while operating rate is closer to how much of the cluster is filled with customer workloads. The reports did not detail how either metric is defined sourcesource. So rather than reading one as better, it is more accurate to say Huawei highlighted training efficiency and Moore Threads highlighted commercial demand.

Moore Threads also said it achieved 92% of the performance of an NVIDIA H800 configuration in a DeepSeek V4 Flash inference test source. However, measurement conditions such as batch size, precision and number of cards were not given in the report source. The takeaway for practitioners is clear: percentages released by vendors cannot be compared with each other until their definitions and conditions are confirmed.

How solid are the signals of a move to training?

The center of the training-side evidence is the DeepSeek V5 news. According to a 科创板日报 (STAR Market Daily) article reposted on Reddit, the outlet's reporter heard from several supply chain sources that DeepSeek plans to pretrain V5 on Huawei Ascend chips source. The article explained that Ascend had so far been used mainly for inference with DeepSeek models, and that it was first deployed at scale around the 2025 Spring Festival source. DeepSeek and Huawei have not officially confirmed this source.

Broken down by certainty, the evidence differs sharply. On the inference side, there are operating figures from a commercial cluster source and an actual deployment history source. On the training side, there is only one utilization figure stated by a company source and a secondhand report of plans via anonymous sources source. The scale and timeline of training are also unknown source.

So this article reads the current moment not as "training has already moved over" but as a stage where "attempts to move into training are starting to be reported." Pretraining requires many chips to run as a single job for a long time, so it can be seen as placing higher demands on chip-to-chip connections (interconnect) and software stability than inference. The first point to check this question will be when DeepSeek V5 is actually released and its training environment is disclosed.

What is moving outside the chips?

Chip performance alone is not enough to gauge how self-sufficient China is, because design tools, demand and policy are moving at the same time.

On the design-tool side, there is a report from SCMP. Empyrean, China's largest EDA company, disclosed that it will acquire a 50.38% stake in the startup Easy-Logic for 218.8 million yuan (about $33 million) source. SCMP read the acquisition as a move to shore up a weak link in EDA and to support Huawei's chip design principle, the 'Tau Scaling Law' source.

On the demand side, CCID said in an October 31 report that inference data in Chinese AI had overtaken training data for the first time sourcesource. According to Pandaily, daily token calls exceeded 175 trillion as of June, more than 1,000 times the level in early 2024 source. The reports do not describe in detail how this was counted source. If the figures hold, a large share of demand for domestic chips can be seen as coming from inference, which fits with Moore Threads putting its inference test results front and center source.

On the policy side, Cloud Security Alliance Labs published an analysis note treating China's Ministry of Commerce Announcement No. 48 as China's first export control on frontier AI source. The collected material does not confirm the scope of the controls or when they take effect source. This week, this site's tally found the topic covered by 1 news outlet and 3 community posts. Teams outside China that use Chinese open-weight models should start by checking the original text.