Chinese AI models are popular globally and are beating U.S. rivals in some areas

China’s attempts to dominate the world of artificial intelligence could be paying off, with industry insiders and technology analysts telling CNBC that Chinese AI models are already hugely popular and are keeping pace with — and even surpassing — those from the U.S. in terms of performance.

AI has become the latest battleground between the U.S. and China, with both sides considering it a strategic technology. Washington continues to restrict China’s access to leading-edge chips designed to help power artificial intelligence amid fears that the technology could threaten U.S. national security.

It’s led China to pursue its own approach to boosting the appeal and performance of its AI models, including relying on open-sourcing technology and developing its own super-fast software and chips.

Like some of the leading U.S. firms in the space, Chinese AI firms are developing so-called large language models, or LLMs, which are trained on huge amounts of data and underpin applications such as chatbots.

Unlike OpenAI’s models which power the hugely popular ChatGPT, however, many of these Chinese companies are developing open-source, or open-weight, LLMs which developers can download and build on top of for free and without stringent licensing requirements from the inventor.

On Hugging Face, a repository of LLMs, Chinese LLMs are the most downloaded, according to Tiezhen Wang, a machine learning engineer at the company. Qwen, a family of AI models created by Chinese e-commerce giant Alibaba, is the most popular on Hugging Face, he said.

“Qwen is rapidly gaining popularity due to its outstanding performance on competitive benchmarks,” Wang told CNBC by email.

He added that Qwen has a “highly favorable licensing model” which means it can be used by companies without the need for “extensive legal reviews.”

Qwen comes in various sizes, or parameters, as they’re known in the world of LLMs. Large parameter models are more powerful but have higher computational costs, while smaller ones are cheaper to run.

“Regardless of the size you choose, Qwen is likely to be one of the best-performing models available right now,” Wang added.

DeepSeek, a start-up, also made waves recently with a model called DeepSeek-R1. DeepSeek said last month that its R1 model competes with OpenAI’s o1 — a model designed for reasoning or solving more complex tasks.

These companies claim that their models can compete with other open-source offerings like Meta‘s Llama, as well as closed LLMs such as those from OpenAI, across various functions.

“In the last year, we’ve seen the rise of open source Chinese contributions to AI with really strong performance, low cost to serve and high throughput,” Grace Isford, a partner at Lux Capital, told CNBC by email.

Open sourcing a technology serves a number of purposes, including driving innovation as more developers have access to it, as well as building a community around a product.

It is not only Chinese firms that have launched open-source LLMs. Facebook parent Meta, as well as European start-up Mistral, also have open-source versions of AI models.

But with the technology industry caught in the crosshairs of the geopolitical battle between Washington and Beijing, open-source LLMs give Chinese firms another advantage: enabling their models to be used globally.

“Chinese companies would like to see their models used outside of China, so this is definitively a way for companies to become global players in the AI space,” Paul Triolo, a partner at global advisory firm DGA Group, told CNBC by email.

While the focus is on AI models right now, there is also debate over what applications will be built on top of them — and who will dominate this global internet landscape going forward.

“If you assume these frontier base AI models are table stakes, it’s about what these models are used for, like accelerating frontier science and engineering technology,” Lux Capital’s Isford said.

Today’s AI models have been compared to operating systems, such as Microsoft’s Windows, Google‘s Android and Apple‘s iOS, with the potential to dominate a market, like these companies do on mobile and PCs.

If true, this makes the stakes for building a dominant LLM higher.

“They [Chinese companies] perceive LLMs as the center of future tech ecosystems,” Xin Sun, senior lecturer in Chinese and East Asian business at King’s College London, told CNBC by email.

“Their future business models will rely on developers joining their ecosystems, developing new applications based on the LLMs, and attracting users and data from which profits can be generated subsequently through various means, including but far beyond directing users to use their cloud services,” Sun added.

AI models are trained on vast amounts of data, requiring huge amounts of computing power. Currently, Nvidia is the leading designer of the chips required for this, known as graphics processing units (GPUs).

Most of the leading AI companies are training their systems on Nvidia’s most high-performance chips — but not in China.

Over the past year or so, the U.S. has ramped up export restrictions on advanced semiconductor and chipmaking equipment to China. It means Nvidia‘s leading-edge chips cannot be exported to the country and the company has had to create sanction-compliant semiconductors to export.

Despite, these curbs, however, Chinese firms have still managed to launch advanced AI models.

“Major Chinese technology platforms currently have sufficient access to computing power to continue to improve models. This is because they have stockpiled large numbers of Nvidia GPUs and are also leveraging domestic GPUs from Huawei and other firms,” DGA Group’s Triolo said.

Indeed, Chinese companies have been boosting efforts to create viable alternatives to Nvidia. Huawei has been one of the leading players in pursuit of this goal in China, while firms like Baidu and Alibaba have also been investing in semiconductor design.

“However, the gap in terms of advanced hardware compute will become greater over time, particularly next year as Nvidia rolls out its Blackwell-based systems that are restricted for export to China,” Triolo said.

Lux Capital’s Isford flagged that China has been “systematically investing and growing their whole domestic AI infrastructure stack outside of Nvidia with high-performance AI chips from companies like Baidu.”

“Whether or not Nvidia chips are banned in China will not prevent China from investing and building their own infrastructure to build and train AI models,” she added.