The Trump administration's export control policies governing the sale of AI accelerators to China haven't stopped homegrown Huawei from filling the void. In a Q&A-style release this week, the company's rotating chairman Eric Xu claimed that its Ascend line of neural processing units (NPUs) had exceeded Nvidia in Chinese market share.
"It's pretty hard to collect data about the market share of Nvidia in China, but based on the data we have collected, Ascend has surpassed Nvidia," he said. "Chinese people have a keen sense of urgency, and will not accept a future in which others decide whether or not we can have access to certain products."
Since taking office, the Trump administration has done an about-face on export controls. After imposing new licensing requirements on Nvidia's and AMD's China-spec accelerators in April 2025, the US Commerce Department reversed course that summer, later reaching an arrangement to cut the US government in on a percentage of certain China-bound chip sales. By late last year, the Trump administration loosened restrictions further, announcing that Nvidia would be allowed to sell its more potent H200-series parts to approved Chinese customers for the first time ever.
However, the damage was already done. While Uncle Sam had given Jensen Huang its blessing to resume sales, the initial blockade of H20s earlier in the year had set off alarm bells in Beijing. Government officials there began pressuring bit barn operators to transition away from foreign accelerators in favor of homegrown alternatives.
During Nvidia's Q2 earnings call, executives revealed that while the company had begun shipments of a small volume of H200 accelerators to the region, they amounted to less than 1 percent of its datacenter revenues.
"Even though our chips may be less advanced, at least their supply is assured, so that you don't have to worry about chip supply day in and day out," Xu said. "For the Chinese government, for the domestic industry, and for Huawei, the path forward is undoubtedly to push for full self-sufficiency in terms of chips and the entire semiconductor value chain."
As we recently discussed, while Huawei's latest accelerators still fall short of Nvidia's or AMD's on paper, they make up for that deficit with scale. Huawei is currently deploying a 256,000-card Atlas 950 SuperCluster, while its newer architecture is designed to support training and inference for multi-trillion-parameter models and scale to as many as one million NPUs.
However, as Xu notes, Huawei likely won't be a threat to Nvidia outside of China for a while.
"We don't have enough capacity to satisfy the demand in China. We don't have plans to expand the international market in a fully-fledged way," he said, downplaying reports that Huawei had begun supplying friendly nations with its chips.
"The balance between satisfying the needs of domestic customers and those of international customers is actually quite easy," he said when asked about the use of Ascend accelerators to power Malaysia's sovereign AI initiative. "We prioritize Chinese customers who are in urgent demand for compute; outside of China we only supply a very limited number of customers who either have no access to alternative solutions or want an alternative solution."
Along with pressure from government officials to transition away from foreign chips, changing software paradigms are making the transition easier for model devs, like DeepSeek, which has been working with Huawei's chips for well over a year now.
"The first barrier encountered by Ascend is actually not hardware itself," said Liao Heng, chief scientist of Huawei's HiSilicon. "It's primarily the ecosystem barrier, because two years ago there was still a tremendous software barrier for Ascend."
Liao is referring to what's often called the CUDA moat, a software hurdle not unique to China but faced by essentially every competing chipmaker.
"There have been drastic changes over the past 18 months," Liao said.
He explained that, two years ago, AI training was dominated by PyTorch running atop CUDA, but changes in model architectures have pushed model devs to "mega-kernel-style programming."
"CUDA was no longer deemed important by the frontier labs, nowhere near as much as two years ago," he said.
To this end, DeepSeek reportedly released several software tools this week aimed at making Huawei’s Ascend accelerators more accessible to other AI labs. ®