(Image credit: Photo by editor Lin Zhijia)

AsianFin -- Ant Group, an affiliate company of Chinese conglomerate Alibaba Group, has developed AI training techniques using Chinese-made semiconductors that could reduce costs by 20%.

The fintech giant leveraged domestic chips from affiliates such as Alibaba Group Holding Ltd. and Huawei Technologies Co. to train models through the Mixture of Experts (MoE) machine learning approach, the sources said.

These models reportedly achieved performance levels comparable to those trained on Nvidia's H800 GPUs. While Ant continues to use Nvidia chips for AI development, it has increasingly turned to alternatives, including Advanced Micro Devices Inc. and other Chinese semiconductor providers, one source added.

Ant's latest development signals its entry into the intensifying competition between Chinese and U.S. firms to develop cutting-edge AI models. This race has gained momentum after DeepSeek demonstrated that highly capable AI models can be trained at a fraction of the cost spent by OpenAI and Alphabet Inc.'s Google.

The shift also underscores how Chinese companies are working to reduce reliance on Nvidia's advanced semiconductors, which are subject to U.S. export restrictions. While the H800 is not the most advanced Nvidia GPU, it remains one of the most powerful AI chips currently banned from sale to China.

This month, Ant published a research paper claiming that its AI models outperformed meta Platforms Inc. by certain benchmarks. While these claims have not be independently verified, such advancements could be significant if /confirm/ied. If Ant's technology performs as advertised, it could enhance China's AI development by lowering the cost of inference and supporting a wider range of AI applications.

The MoE machine learning technique, which Ant has adopted, has gained traction among AI leaders such as Google and DeepSeek. This method breaks down tasks into smaller specialized segments, much like a team of experts each handling different parts of a job, leading to more efficient processing.