Even though Nvidia's AI GPUs and rack-scale solutions remain the most sought-after AI accelerators, curbs set on exports of Nvidia's AI processors to China, first by the White House and then by Beijing, are having a drastic effect on the company's presence in the People's Republic. As a result, the company's share in China could drop to just 8% in the coming years as domestic suppliers can satisfy around 80% of local demand, reports Nikkei, citing analysis from Bernstein.
"The new products meet the needs of domestic developers," said Zhang Jianzhong, chief executive of Moore Threads, at a news conference while announcing the codenamed Huashan product, the company's first GPU dedicated solely for the acceleration of AI workloads. "There will be no more need to wait for advanced products from overseas."
Moore Threads' Huashan can compete against Nvidia's Hopper H100 and H200 products, the company's previous-generation AI accelerators that the U.S. recently allowed to export to China, but with some serious strings attached. However, they are considerably slower than Nvidia's existing Blackwell B200 and B300 GPUs, which are barred from export to the People's Republic. Meanwhile, Huawei's AI CloudMatrix 384 can beat both GB200 NVL72 and GB300 NVL72 systems in BF16 FLOPS, a popular format used for AI training, albeit with four times more power consumption. The company's next-generation Atlas 950 SuperCluster, based on 524,288 Ascend 950DT AI accelerators, is projected to offer up to 524 FP8 ExaFLOPS for AI training and up to 1 FP4 ZettaFLOPS for AI inference (MXFP4 to be more specific) sometimes in 2026 – 2027 and 4 ZettaFLOPS by the end of 2028. This is still behind leading Blackwell-based clusters, such as Oracle's OCI Supercluster running 131,072 B200 GPUs and offering peak performance of up to 2.4 FP4 ZettaFLOPS for inference, but it is evident that Chinese developers are rapidly increasing the performance of their AI hardware.
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