SHANGHAI: Jack Ma-backed Ant Group Co uses Chinese-made semiconductors to develop techniques for training artificial intelligence (AI) models that would cut costs by 20%, according to people familiar with the matter.
Ant used domestic chips, including from affiliate Alibaba Group Holding Ltd and Huawei Technologies Co, to train models using the so-called Mixture of Expert (MoE) machine learning approach, the people said.
It got results similar to those from Nvidia Corp chips like the H800, they said, asking not to be named as the information isn’t public.
Ant is still using Nvidia for AI development but is now relying mostly on alternatives, including from Advanced Micro Devices Inc and Chinese chips for its latest models, one of the people said.
The models mark Ant’s entry into a race between Chinese and US companies that’s accelerated since DeepSeek demonstrated how capable models can be trained for far less than the billions invested by OpenAI and Alphabet Inc’s Google.
It underscores how Chinese companies are trying to use local alternatives to the most advanced Nvidia semiconductors.
While not the most advanced, the H800 is a relatively powerful processor and currently barred by the United States from China.
The company published a research paper this month that claimed its models at times outperformed Meta Platforms Inc in certain benchmarks, which Bloomberg News hasn’t independently verified.
But if they work as advertised, Ant’s platforms could mark another step forward for Chinese AI development by slashing the cost of inferencing or supporting AI services.
As companies pour significant money into AI, MoE models have emerged as a popular option, gaining recognition for their use by Google and Hangzhou startup DeepSeek, among others.
That technique divides tasks into smaller sets of data, very much like having a team of specialists who each focus on a segment of a job, making the process more efficient.
Ant declined to comment in an emailed statement. However, the training of MoE models typically relies on high-performing chips like the graphics processing units (GPUs) Nvidia sells.
The cost has, to date, been prohibitive for many small firms and limited broader adoption.
Ant has been working on ways to train large language models more efficiently and eliminate that constraint.
Its paper title makes that clear, as the company sets the goal to scale a model “without premium GPUs”. — Bloomberg
