This article is provided for informational purposes only and does not constitute investment advice. The author does not hold any shares in the companies referenced.
Challenges Facing Chinese AI Data Centers in Transitioning to Domestic Chips
According to a recent report from the South China Morning Post, Chinese AI data centers are encountering significant hurdles in their attempts to transition from NVIDIA’s AI GPUs to alternatives offered by Huawei. A directive from the Chinese government stipulates that publicly funded AI data centers must use a minimum of 50% domestic chips, aimed at diminishing reliance on foreign semiconductor technologies.
These regulations have their roots in guidelines established last year by the Shanghai municipality, necessitating that the city’s computing facilities incorporate at least half of their chips from China. As of this year, these guidelines have expanded into nationwide mandates that affect all AI data centers across the country.
Concerns with NVIDIA GPUs and the Transition to Huawei Alternatives
The controversy surrounding NVIDIA’s H20 GPUs has influenced this shift. Following the Trump administration’s decision to allow NVIDIA to sell its GPUs to China, concerns emerged about potential backdoors and vulnerabilities within these chips—claims that NVIDIA has denied. Nonetheless, reports indicate that the Chinese government is cautious about foreign hardware due to these security apprehensions. Concurrently, there seems to be a growing recognition within China of the need to reduce dependencies on international chips to support its AI ambitions.
The latest SCMP article highlights new mandates requiring state-run computing infrastructures to depend on domestic chips for at least 50% of their needs—a rule stemming from revised municipal regulations. As of 2024, these guidelines will shape how data facilities operate, reflecting a broader strategic shift toward technological self-sufficiency.
The primary domestic alternatives to NVIDIA’s GPUs are those manufactured by Huawei, in collaboration with SMIC, which is restricted to using older 7-nanometer technology due to U. S.sanctions that limit access to advanced semiconductor fabrication techniques and equipment.
While NVIDIA’s chips are vital for training advanced AI models, Huawei’s processors can handle deployment, yet the government-mandated transition is not without its complications. Many cluster operators find themselves in a challenging position as their AI solutions were initially developed around NVIDIA’s technology.
A significant factor contributing to these operational challenges is the difference in software ecosystems. NVIDIA’s GPUs utilize the CUDA platform, whereas Huawei’s chips rely on the CANN framework. This fundamental disparity poses a barrier for data centers that must now integrate Huawei hardware while retaining the functionality of AI models originally designed on NVIDIA’s infrastructure.
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