Chinese companies that are dedicated to the development of large artificial intelligence models (AI) are trapped. On the one hand they are being forced to deal with the export restrictions of the GPUs imposed by the US government. And, in addition, they are subject to His own dependence on American technology. A priori the optimal solution for them would be to stop buying Nvidia and other US companies their chips for AI, and getting “comparable” “comparable” GPUs that propose Huawei or Moore Threads, among other Chinese companies.
However, as explained in his article for Foreign Policy, the American analyst Kyle Chan, the scenario they face is more complicated than it seems. And it is that abandoning Nvidia in practice is very difficult. According to Chan, Tencent, Bytedance, Alibaba and other Chinese companies prefer GPUs for NVIDIA because their performance is greater, especially when facing the training processes of their AI models. However, they especially opt for the chips of this American company thanks to CUDA (Compute Unified Device Architecture).
CUDA is the most devastating Nvidia weapon to continue leading in AI Hardware
Most of the AI projects that are currently being developed are implemented on CUDA. This technology brings together the compiler and development tools used by programmers to develop their software for NVIDIA GPUs, and replace it with another option in the projects that are already underway it is a problem. Huawei, who aspires to get an important portion of this market in China, has Cann (Compute Architecture for Neural Networks), which is its alternative to CUDA, but for the moment CUDA dominates the market.
“China must develop an alternative system to achieve self -sufficiency in AI”
This Declaration of Li Guojie, a computer scientist at the Chinese Academy of Sciences that is considered an authority in China, clearly expresses how important the tools that I just mentioned in the IA models development ecosystem: “China must develop an alternative system for achieve self -sufficiency in AI (…) Deepseek has had an impact on the CUDA ecosystem, but has not completely overcome it because barriers persist. In the long term we need to establish a set of software tool systems for the controllable that exceed CUDA. “
This is undoubtedly one of the great challenges that China faces in this area, and probably its best option is Cann. During the last five months Huawei has launched two GPUs for the very competitive and is about to take a very important step: it will position Cann as an open source tools kit. Its purpose is, according to Eric Xu Zhijun, Rotary President of Huawei, “accelerating the innovation of developers and getting the chips of the Ascend family to be easier to use.”
Xu Zhijun does not mention it expressly, but what his strategy pursues in the background is to increase the competitiveness of the Huawei ecosystem attacking Nvidia where he is stronger. In addition, it has already begun to discuss with the main actors of the AI industry of China, as well as with its business partners, universities and research institutions How to build your ecosystem Open source ascend. If this initiative thrives, and presumably will, it will represent a very important step forward on the road to China’s technological independence.
Image | Nvidia | Huawei
More information | Foreign Policy
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