China would love a domestic Nvidia rival — but that’s proving quite the challenge
The flags of China and the USA are being displayed on a smartphone, with an NVIDIA chip visible in the background.
Raa | Nurphoto | Getty Images
Chinese companies are ramping up efforts to produce a viable alternative to Nvidia’s chips that power artificial intelligence as Beijing continues its efforts to wean itself off American technology.
U.S. sanctions slapped on China over the past few years, along with Nvidia‘s dominance in the space, have provided big challenges for Bejing’s efforts, at least in the short term, analysts told CNBC.
Nvidia’s well-documented boom has been driven by large cloud computing players buying its server products which contain its graphics processing units, or GPUs. These chips are enabling companies, such as ChatGPT maker OpenAI, to train their huge AI models on massive amounts of data.
These AI models are fundamental to applications like chatbots and other emerging AI applications.
The U.S. government has restricted the export of Nvidia’s most advanced chips to China since 2022, with restrictions tightening last year.
Such semiconductors are key to China’s ambitions to become a leading AI player.
CNBC spoke to analysts who identified some of China’s leading contenders that are looking to challenge Nvidia, including technology giants Huawei, Alibaba and Baidu and startups such as Biren Technology and Enflame.
The overarching view is that they are lagging behind Nvidia at this point.
“These companies have made notable progress in developing AI chips tailored to specific applications (ASICs),” Wei Sun, a senior analyst at Counterpoint Research, told CNBC.
“However, competing with Nvidia still presents substantial challenges in technological gaps, especially in general-purpose GPU. Matching Nvidia in short-term is unlikely.”
China’s key challenges
Chinese firms have a “lack of technology expertise”, according to Sun, highlighting one of the challenges.
However, it’s the U.S. sanctions and their knock-on effects that pose the biggest roadblocks to China’s ambitions.
Some of China’s leading Nvidia challengers have been placed on the U.S. Entity List, a blacklist which restricts their access to American technology. Meanwhile, a number of U.S. curbs have restricted key AI-related semiconductors and machinery from being exported to China.
China’s GPU players all design chips and rely on a manufacturing company to produce their chips. For a while, this would have been Taiwan Semiconductor Manufacturing Co., or TSMC. But U.S. restrictions mean many of these firms cannot access the chips made by TSMC.
They therefore have to turn to SMIC, China’s biggest chipmaker, whose technology remains generations behind TSMC. Part of the reason why it’s lagging behind, is because Washington has restricted SMIC’s access to a key piece of machinery from Dutch firm ASML, which is required to manufacture the most advance chips.
Meanwhile, Huawei has been pushing development of more advanced chips for its smartphones and AI chips, which is taking up capacity at SMIC, according to Paul Triolo, a partner at consulting firm Albright Stonebridge.
“The key bottleneck will be domestic foundry leader SMIC, which will have a complex problem of dividing limited resources for its advanced node production between Huawei, which is taking up the lion’s share currently, the GPU startups, and many other Chinese design firms which have been or may be cutoff from using global foundry leader TSMC to manufacture their advanced designs,” Triolo told CNBC.
Nvidia is more than just GPUs
Nvidia has found success due to its advanced semiconductors, but also with its CUDA software platform that allows developers to create applications to run on the U.S. chipmaker’s hardware. This has led to the development of a so-called ecosystem around Nvidia’s products that others might find hard to replicate.
“This is the key, it is not just about the hardware, but about the overall ecosystem, tools for developers, and the ability to continue to evolve this ecosystem going forward as the technology advances,” Triolo said.
Huawei leading the pack
Triolo identified Huawei as one of the leaders in China with its Ascend series of data center processors.
The firm’s current generation of chip is called the Ascend 910B, and the company is gearing up to launch the Ascend 910C, which could be on par with Nvidia’s H100 product, according to a Wall Street Journal report in August.
In its annual report earlier this year, Nvidia explicitly identified Huawei, among other companies, as a competitor in areas such as chips, software for AI and networking products.
In the area of software and building a developer community, Huawei “holds lots of advantages,” Triolo said. But it faces similar challenges to the rest of the industry in trying to compete with Nvidia.
“The GPU software support ecosystem is much more entrenched around Nvidia and to a lesser degree AMD, and Huawei faces major challenges, both in producing sufficient quantities of advanced GPUs such as part of the Ascend 910C, and continuing to innovate and improve the performance of the hardware, given U.S. export controls that are limiting the ability of SMIC to produce advanced semiconductors,” Triolo said.
Chip IPOs ahead?
The challenges facing China’s Nvidia competitors have been evident over the past two years. In 2022, Biren Technology carried out a round of layoffs, followed by Moore Threads the year after, with both companies blaming U.S. sanctions.
But startups are still holding out hope, looking to raise money to fund their goals. Bloomberg reported last week that Enflame and Biren are both looking to go public to raise money.
“Biren and the other GPU startups are staffed with experienced industry personnel from Nvidia, AMD, and other leading western semiconductor companies, but they have the additional challenge of lacking the financial depth that Huawei has,” Triolo said.
“Hence both Biren and Enflame are seeking IPOs in Hong Kong, to raise funding for additional hiring and expansion.”
Technology
A ‘seismic’ Nvidia shift, AI chip shortages and how it’s threatening to hike gadget prices
The logo of an Apple Store is seen reflected on the glass exterior of a Samsung flagship store in Shanghai, China Monday, Oct. 20, 2025.
Wang Gang | Feature China | Future Publishing | Getty Images
The cost of your smartphone might rise, analysts are warning, as the AI boom clogs up supply chains and a recent change by Nvidia to its products could make it worse.
AI data centers, on which tech giants globally are spending hundreds of billions of dollars, require chips from suppliers, like Nvidia, which relies on many different components and companies to create its coveted graphics processing units.
But other companies like AMD, the hyperscalers like Google and Microsoft, and other component suppliers all rely on this supply chain.
Many parts of the supply chain can’t keep up with demand, and it’s slowing down components that are critical for some of the world’s most popular consumer electronics. Those components are seeing huge spikes in prices, threatening price rises for the end product and could even lead to shortages of some devices.
“We see the rapid increase in demand for AI in data centers driving bottlenecks in many areas,” Peter Hanbury, partner in the technology practice at Bain & Company, told CNBC.
Where is the supply chain clogged?
One of the starkest assessments came from Alibaba CEO Eddie Wu, CEO of Chinese tech giant Alibaba.
Wu, whose company is building its own AI infrastructure and designs its own chips, said last week that there are shortages across semiconductor manufacturers, memory chips and storage devices like hard drives.
“There is a situation of undersupply,” Wu said, adding that the “supply side is going to be a relatively large bottleneck.” He added this could last two to three years.
Bain and Co.’s Hanbury said there are shortages of hard disk drives, or HDDs, which store data. HDDs are used in the data center. These are preferred by hyperscalers,: big companies like Microsoft and Google. But, with HDDs at capacity, these firms have shifted to using solid-state drives, or SSDs, another type of storage device.
However, these SSDs are key components for consumer electronics.
The other big focus is on a type of chip under the umbrella of memory called dynamic random-access memory or DRAM. Nvidia’s chips use high-bandwidth memory which is a type of chip that stacks multiple DRAM semiconductors.
Memory prices have surged as a result of the huge demand and lack of supply. Counterpoint Research said it expects memory prices to rise 30% in the fourth quarter of this year and another 20% in early 2026. Even small imbalances in supply and demand can have major knock on effects on memory pricing. And because of the demand for HBM and GPUs, chipmakers are prioritizing these over other types of semiconductors.
“DRAM is certainly a bottleneck as AI investments continue to feed the imbalance between demand and supply with HBM for AI being prioritized by chipmakers,” MS Hwang, research director at Counterpoint Research, told CNBC.
“Imbalances of 1-2% can trigger sharp price increases and we’re seeing that figure hitting 3% levels at the moment – this is very significant.”
Why are there issues?
Building up capacity in various areas of the semiconductor supply chain can be capital-intensive. And it’s an industry that’s known to be risk-averse and did not add the capacity necessary to meet the projections provided by key industry players, Bain & Co.’s Hanbur said.
“The direct cause of the shortage is the rapid increase in demand for data center chips,” Hanbury said.
“Basically, the suppliers worried the market was too optimistic and they did not want to overbuild very expensive capacity so they did not build to the estimates provided by their customers. Now, the suppliers need to add capacity quickly but as we know, it takes 2-3 years to add semiconductor manufacturing fabs.”
Nvidia at the center
A lot of attention is on Nvidia given it dominates when it comes to the chips that are being put into AI data centers.
It is a huge customer of high bandwidth memory, for example. And its products are manufactured by TSMC which also has other major customers like Apple.
But analysts are focused on a change Nvidia has made to its products that has the potential to add major pressure to consumer electronics supply chains. The U.S. giant is increasingly shifting toward using a type of memory in its products called Low-Power Double Data Rate (LPDDR). This is seen as more power efficient than the previous Double Data Rate, or DDR memory.
The problem is, Nvidia is increasingly using the latest generation of LPDDR memory, which is also used by high-end consumer electronics makers such as Samsung and Apple.
Typically, the industry would just be dealing with demand for this product from a handful of big electronics players. But now Nvidia, which has huge scale, is entering the mix.
“We also see a bigger risk on the horizon is with advanced memory as Nvidia’s recent pivot to LPDDR means they’re a customer on the scale of a major smartphone maker — a seismic shift for the supply chain which can’t easily absorb this scale of demand,” Hwang from Counterpoint Research said.
How AI boom is impacting consumer electronics
Here’s the link between all of this.
From chip manufacturers like TSMC, Intel and Samsung, there is only so much capacity. If there is huge demand for certain types of chips, then these companies will prioritize those, especially from their larger customers. That can lead to shortages of other types of semiconductors elsewhere.
Memory chips, in particular DRAM which has seen prices shoot up, is of particular concern because it’s used in so many devices from smartphones to laptops. And this could lead to price rises in the world’s favorite electronics.
DRAM and storage represent around 10% to 25% of the bill of materials for a typical PC or smartphone, according to Hanbury of Bain & Co. A price increase of 20% to 30% in these components would increase the total bill of materials costs by 5% to 10%.
“In terms of timing, the impact will likely start shortly as component costs are already increasing and likely accelerate into next year,” Hanbury said.
On top of this, there is now demand from players involved in AI data centers like Nvidia, for components that would have typically been used for consumer devices such as LPDDR which adds more demand to a supply constrained market.
If electronics firms can’t get their hands on the components needed for their devices because they’re in short supply or going toward AI data centers, then there could be shortages of the world’s most popular gadgets.
“Beyond the rise in cost there’s a second issue and that’s the inability to secure enough components, which constrains the production of electronic devices,” Counterpoint Research’s Hwang said.
What are tech firms saying?
A number of electronics companies have warned about the impact they are seeing from all of this.
Xiaomi, the third-biggest smartphone vendor globally, said it expects that consumers will see “a sizeable rise in product retail prices,” according to a Reuters reported this month.
Jeff Clark, chief operating officer at Dell, this month said the price rises of components is “unprecedented.”
“We have not seen costs move at the rate that we’ve seen,” Clark said on an earnings call, adding that the pressure is seen across various types of memory chips and storage hard drives.
The unintended consequences
The AI infrastructure players are using similar chips to those being used in consumer electronics. These are often some of the more advanced semiconductors on the market.
But there are legacy chips which are manufactured by the same companies that the AI market is relying on. As these manufacturers shift attention to serving their AI customers, there could be unintended consequences for other industries.
“For example, many other markets depend on the same underlying semiconductor manufacturing capabilities as the data center market” including automobiles, industrials and aerospace and defense, which “will likely see some impact from these price increases as well,” Hanbury said.
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