(Image credit: Huawei)

China has begun to assemble a list of government-approved AI hardware suppliers that is designed to encourage public sector organizations to prioritize locally developed artificial intelligence processors, reports the Financial Times. At present, the list only includes Cambricon and Huawei, but does not list any foreign companies, such as AMD or Nvidia, perhaps highlighting China’s government attitude towards President Trump’s plans to let Nvidia sell its H200 processors to customers in China.

The new list, which will be distributed to ministries, state-owned companies, and public institutions, expands the Information Technology Innovation List (Xinchuang) to include domestic AI processors alongside previously added categories such as local x86-replacement CPUs and homegrown operating systems that replace Microsoft Windows. This list effectively outlines which hardware and software platforms government bodies may purchase, which, to a large degree, defines where billions of dollars per annum are spent by Chinese government-controlled entities.

The Ministry of Industry and Information Technology did not comment on the updated procurement rules, FT reports, but the policy direction seems clear: China intends to accelerate displacement of U.S.-designed AI accelerators with homegrown alternatives within the state sector.

For China, developing its AI prowess and semiconductor self-sufficiency at the same time creates a dilemma. On the one hand, Nvidia’s hardware offers higher performance and a better software stack, which greatly helps Chinese companies train larger AI models. Furthermore, many public-sector workloads remain tightly integrated with Nvidia’s CUDA ecosystem, which complicates migration to alternative architectures, such as those designed by Cambricon or Huawei. On the other hand, using domestic hardware and software for building homegrown AI ecosystem enables Chinese companies to set up their own AI standards and eventually develop more competitive AI accelerators.

Commercial companies such as Alibaba and Tencent use Nvidia’s hardware to maintain their competitiveness — for them, building their AI ecosystem is more important than China’s semiconductor self-sufficiency. While the Chinese government may ban American AI accelerators (like it did with Nvidia’s H20), these companies can still use them in the cloud, avoiding U.S. sanctions and sustaining their dependance on American technology.

To make it more appealing for China’s cloud giants to use domestic hardware not only for inference, but also for training, China has expanded energy subsidies for these companies. Operators of large-scale data centers can now receive a 50% discount on electricity when deploying Chinese-made AI accelerators. This measure is designed to compensate the lower power efficiency of Chinese AI processors relative to Nvidia’s AI GPUs while preserving performance they need to train larger AI models and then execute them.

Perhaps the biggest question is not whether Chinese public and private companies are willing to replace a significant portion of AI hardware developed in America with domestic solutions, but rather whether the Chinese industry can actually produce enough AI processors to satisfy the potential demands of the domestic AI sector.

At present, the only company in China that can make chips that can compete against those fabbed by TSMC is SMIC. SMIC’s capacity is utilized by 95.8% and it cannot increase its output significantly, as, thanks to sanctions imposed by the U.S. and Dutch governments, it cannot buy advanced fab tools. It is expected that eventually Huawei will also build its own fab that will rely primarily on domestic equipment, so the country’s output of advanced chips will increase, though it is completely unclear when this fab is set to come online.

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Anton Shilov is a contributing writer at Tom’s Hardware. Over the past couple of decades, he has covered everything from CPUs and GPUs to supercomputers and from modern process technologies and latest fab tools to high-tech industry trends.