While limited quantities have already been delivered, mass deployment would mark an important step for Chinese firms seeking domestic alternatives to US-made semiconductors.

The move comes at a time when Chinese developers face tighter restrictions on access to Nvidia hardware. The US government recently informed Nvidia that sales of its H20 AI chip to China require an export licence. That’s left developers in China looking for options that can support large-scale training and inference workloads.

The Huawei Ascend 910C chip isn’t built on the most advanced process nodes, but it represents a workaround. The chip is essentially a dual-package version of the earlier 910B, with two processors to double the performance and memory. Sources familiar with the chip say it performs comparably to Nvidia’s H100.

Rather than relying on cutting-edge manufacturing, Huawei has adopted a brute-force approach, combining multiple chips and high-speed optical interconnects to scale up performance. This approach is central to Huawei’s CloudMatrix 384 system, a full rack-scale AI platform for training large models.

The CloudMatrix 384 features 384 Huawei Ascend 910C chips deployed in 16 racks comprising of 12 compute racks and four networking. Unlike copper-based systems, Huawei’s platform is uses optical interconnects, enabling high-bandwidth communication between components of the system. According to analysis from SemiAnalysis, the architecture includes 6,912 800G LPO optical transceivers to form an optical all-to-all mesh network.

This allows Huawei’s system to deliver approximately 300 petaFLOPs of BF16 compute power – outpacing Nvidia’s GB200 NVL72 system, which reaches around 180 BF16 petaFLOPs. The CloudMatrix also claims advantages in higher memory bandwidth and capacity, offering more than double the bandwidth and over 3.6 times the high-bandwidth memory (HBM) capacity.

The gains, however, are not without drawbacks. The Huawei system is predicted to be 2.3 times less efficient per floating point operation than Nvidia’s GB200 and has lower power efficiency per unit of memory bandwidth and capacity. Despite the lower performance per watt, Huawei’s system still provides the infrastructure needed to train advanced AI models at scale.

Sources indicate that China’s largest chip foundry, SMIC, is producing some of the main components for the 910C using its 7nm N+2 process. Yield levels remain a concern, however, and some of the 910C units reportedly include chips produced by TSMC for Chinese firm Sophgo. Huawei has denied using TSMC-made parts.

The US Commerce Department is currently investigating the relationship between TSMC and Sophgo after a Sophgo-designed chip was found in Huawei’s earlier 910B processor. TSMC has maintained that it has not supplied Huawei since 2020 and continues to comply with export regulations.

In late 2023, Huawei began distributing early samples of the 910C to selected technology firms and opened its order books. Consulting firm Albright Stonebridge Group suggested the chip is likely to become the go-to choice for Chinese companies building large AI models or deploying inference capacity, given the ongoing export controls on US-made chips.

While the Huawei Ascend 910C may not match Nvidia in power efficiency or process technology, it signals a broader trend. Chinese technology firms are developing homegrown alternatives to foreign components, even if it means using less advanced methods to achieve similar outcomes.

As global AI demand surges and export restrictions tighten, Huawei’s ability to deliver a scalable AI hardware solution domestically could help shape China’s artificial intelligence future – especially as developers look to secure long-term supply chains and reduce exposure to geopolitical risk.

(Photo via Unsplash)

See also: Huawei’s AI hardware breakthrough challenges Nvidia’s dominance

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Huawei to begin mass shipments of Ascend 910C amid US curbs appeared first on AI News.

If the performance claims prove accurate, the AI hardware breakthrough might reshape the technology landscape at a time when AI development is continuing worldwide, and despite US efforts to limit China’s access to advanced technology.

300 petaflops: Challenging Nvidia’s hardware dominance

The CloudMatrix 384 Supernode is described as a “nuclear-level product,” according to reports from STAR Market Daily cited by the South China Morning Post (SCMP). The hardware achieves an impressive 300 petaflops of computing power, in excess of the 180 petaflops delivered by Nvidia’s NVL72 system.

The CloudMatrix 384 Supernode was specifically engineered to address the computing bottlenecks that have become increasingly problematic as artificial intelligence models continue to grow in size and complexity.

The system is designed to compete directly with Nvidia’s offerings, which have dominated the global market for AI accelerator hardware thus far. Huawei’s CloudMatrix infrastructure was first unveiled in September 2024, and was developed specifically to meet surging demand in China’s domestic market.

The 384 Supernode variant represents the most powerful implementation of AI architecture to date, with reports indicating it can achieve a throughput of 1,920 tokens per second and maintain high levels of accuracy, reportedly matching the performance of Nvidia’s H100 chips, but using Chinese-made components instead.

Developing under sanctions: The technical achievement

What makes the AI hardware breakthrough particularly significant is that it has been achieved despite the severe technological restrictions Huawei has faced since being placed on the US Entity List.

Sanctions have limited the company’s access to advanced US semiconductor technology and design software, forcing Huawei to develop alternative approaches and rely on domestic supply chains.

The core technological advancement enabling the CloudMatrix 384’s performance appears to be Huawei’s answer to Nvidia’s NVLink – a high-speed interconnect technology that allows multiple GPUs to communicate efficiently.

Nvidia’s NVL72 system, released in March 2024, features a 72-GPU NVLink domain that functions as a single, powerful GPU, enabling real-time inference for trillion-parameter models at speeds 30 times faster than previous generations.

According to reporting from the SCMP, Huawei is collaborating with Chinese AI infrastructure startup SiliconFlow to implement the CloudMatrix 384 Supernode in supporting DeepSeek-R1, a reasoning model from Hangzhou-based DeepSeek.

Supernodes are AI infrastructure architectures equipped with more resources than standard systems – including enhanced central processing units, neural processing units, network bandwidth, storage, and memory.

The configuration allows them to function as relay servers, enhancing the overall computing performance of clusters and significantly accelerating the training of foundational AI models.

Beyond Huawei: China’s broader AI infrastructure push

The AI hardware breakthrough from Huawei doesn’t exist in isolation but rather represents part of a broader push by Chinese technology companies to build domestic AI computing infrastructure.

In February, e-commerce giant Alibaba Group announced a massive 380 billion yuan ($52.4 billion) investment in computing resources and AI infrastructure over three years – the largest-ever investment by a private Chinese company in a computing project.

For the global AI community, the emergence of viable alternatives to Nvidia’s hardware could eventually address the computing bottlenecks that have limited AI advancement. Competition in this space could potentially increase available computing capacity and provide developers with more options for training and deploying their models.

However, it’s worth noting that as of the report’s publication, Huawei had not yet responded to requests for comment on these claims.

As tensions between the US and China continue to intensify in the technology sector, Huawei’s CloudMatrix 384 Supernode represents a significant development in China’s pursuit of technological self-sufficiency.

If the performance claims are verified, this AI hardware breakthrough would mean Huawei has achieved computing independence in this niche, despite facing extensive sanctions.

The development also signals a broader trend in China’s technology sector, with multiple domestic companies intensifying their investments in AI infrastructure to capitalise on growing demand and promote the adoption of homegrown chips.

The collective effort suggests China is committed to developing domestic alternatives to American technology in this strategically important field..

See also: Manus AI agent: breakthrough in China’s agentic AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Huawei’s AI hardware breakthrough challenges Nvidia’s dominance appeared first on AI News.

The Alibaba-owned company has used chips from domestic suppliers, including those tied to its parent, Alibaba, and Huawei Technologies to train large language models using the Mixture of Experts (MoE) method. The results were reportedly comparable to those produced with Nvidia’s H800 chips, sources claim. While Ant continues to use Nvidia chips for some of its AI development, one sources said the company is turning increasingly to alternatives from AMD and Chinese chip-makers for its latest models.

The development signals Ant’s deeper involvement in the growing AI race between Chinese and US tech firms, particularly as companies look for cost-effective ways to train models. The experimentation with domestic hardware reflects a broader effort among Chinese firms to work around export restrictions that block access to high-end chips like Nvidia’s H800, which, although not the most advanced, is still one of the more powerful GPUs available to Chinese organisations.

Ant has published a research paper describing its work, stating that its models, in some tests, performed better than those developed by Meta. Bloomberg News, which initially reported the matter, has not verified the company’s results independently. If the models perform as claimed, Ant’s efforts may represent a step forward in China’s attempt to lower the cost of running AI applications and reduce the reliance on foreign hardware.

MoE models divide tasks into smaller data sets handled by separate components, and have gained attention among AI researchers and data scientists. The technique has been used by Google and the Hangzhou-based startup, DeepSeek. The MoE concept is similar to having a team of specialists, each handling part of a task to make the process of producing models more efficient. Ant has declined to comment on its work with respect to its hardware sources.

Training MoE models depends on high-performance GPUs which can be too expensive for smaller companies to acquire or use. Ant’s research focused on reducing that cost barrier. The paper’s title is suffixed with a clear objective: Scaling Models “without premium GPUs.” [our quotation marks]

The direction taken by Ant and the use of MoE to reduce training costs contrast with Nvidia’s approach. CEO Officer Jensen Huang has said that demand for computing power will continue to grow, even with the introduction of more efficient models like DeepSeek’s R1. His view is that companies will seek more powerful chips to drive revenue growth, rather than aiming to cut costs with cheaper alternatives. Nvidia’s strategy remains focused on building GPUs with more cores, transistors, and memory.

According to the Ant Group paper, training one trillion tokens – the basic units of data AI models use to learn – cost about 6.35 million yuan (roughly $880,000) using conventional high-performance hardware. The company’s optimised training method reduced that cost to around 5.1 million yuan by using lower-specification chips.

Ant said it plans to apply its models produced in this way – Ling-Plus and Ling-Lite – to industrial AI use cases like healthcare and finance. Earlier this year, the company acquired Haodf.com, a Chinese online medical platform, to further Ant’s ambition to deploy AI-based solutions in healthcare. It also operates other AI services, including a virtual assistant app called Zhixiaobao and a financial advisory platform known as Maxiaocai.

“If you find one point of attack to beat the world’s best kung fu master, you can still say you beat them, which is why real-world application is important,” said Robin Yu, chief technology officer of Beijing-based AI firm, Shengshang Tech.

Ant has made its models open source. Ling-Lite has 16.8 billion parameters – settings that help determine how a model functions – while Ling-Plus has 290 billion. For comparison, estimates suggest closed-source GPT-4.5 has around 1.8 trillion parameters, according to MIT Technology Review.

Despite progress, Ant’s paper noted that training models remains challenging. Small adjustments to hardware or model structure during model training sometimes resulted in unstable performance, including spikes in error rates.

(Photo by Unsplash)

See also: DeepSeek V3-0324 tops non-reasoning AI models in open-source first

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Ant Group uses domestic chips to train AI models and cut costs appeared first on AI News.

In recent submissions to the US government, the companies warned that Chinese models, such as DeepSeek R1, are becoming more sophisticated and competitive. The submissions, filed in March 2025 in response to a request for input on an AI Action Plan, highlight the growing challenge from China in technological capability and price.

China’s growing AI presence

Chinese state-supported AI model DeepSeek R1 has piqued the interest of US developers. According to OpenAI, DeepSeek demonstrates that the technological gap between the US and China is narrowing. The company described DeepSeek as “state-subsidised, state-controlled, and freely available,” raises concerns about the model’s ability to influence global AI development.

OpenAI compared DeepSeek to Chinese telecommunications company Huawei, warning that Chinese regulations could allow the government to compel DeepSeek to compromise sensitive US systems or infrastructure. Concerns about data privacy were also raised, with OpenAI pointing out that Chinese rules could force DeepSeek to disclose user data to the government, and enhance China’s ability to develop more advanced AI systems.

The competition from China also includes Ernie X1 and Ernie 4.5, released by Baidu, which are designed to compete with Western systems.

According to Baidu, Ernie X1 “delivers performance on par with DeepSeek R1 at only half the price.” Meanwhile, Ernie 4.5 is priced at just 1% of OpenAI’s GPT-4.5 while outperforming it in multiple benchmarks.

DeepSeek’s aggressive pricing strategy is also raising concerns with the US companies. According to Bernstein Research, DeepSeek’s V3 and R1 models are priced “anywhere from 20-40x cheaper” than equivalent models from OpenAI. The pricing pressure could force US developers to adjust their business models to remain competitive.

Baidu’s strategy of open-sourcing its models is also gaining traction. “One thing we learned from DeepSeek is that open-sourcing the best models can greatly help adoption,” Baidu CEO Robin Li said in February. Baidu plans to open-source the Ernie 4.5 series starting June 30, which could accelerate adoption and further increase competitive pressure on US firms.

Cost aside, early user feedback on Baidu’s models has been positive. “[I’ve] been playing around with it for hours, impressive performance,” Alvin Foo, a venture partner at Zero2Launch, said in a post on social media, suggesting China’s AI models are becoming more affordable and effective.

US AI security and economic risks

The submissions also highlight what the US companies perceive as risks to security and the economy.

OpenAI warned that Chinese regulations could allow the government to compel DeepSeek to manipulate its models to compromise infrastructure or sensitive applications, creating vulnerabilities in important systems.

Anthropic’s concerns centred on biosecurity. It disclosed that its own Claude 3.7 Sonnet model demonstrated capabilities in biological weapon development, highlighting the dual-use nature of AI systems.

Anthropic also raised issues with US export controls on AI chips. While Nvidia’s H20 chips meet US export restrictions, they nonetheless perform well in text generation – a important feature for reinforcement learning. Anthropic called on the government to tighten controls to prevent China from gaining a technological edge using the chips.

Google took a more cautious approach, acknowledging security risks yet warned against over-regulation. The company argues that strict AI export rules could harm US competitiveness by limiting business opportunities for domestic cloud providers. Google recommended targeted export controls to protect national security but without disruption to its business operations.

Maintaining US AI competitiveness

All US three companies emphasised the need for better government oversight and infrastructure investment to maintain US AI leadership.

Anthropic warned that by 2027, training a single advanced AI model could require up to five gigawatts of power – enough to power a small city. The company proposed a national target to build 50 additional gigawatts of AI-dedicated power capacity by 2027 and to streamline regulations around power transmission infrastructure.

OpenAI positioned the competition between US and Chinese AI as a contest between democratic and authoritarian AI models. The company argued that promoting a free-market approach would drive better outcomes and maintain America’s technological edge.

Google focused on urging practical measures, including increased federal funding for AI research, improved access to government contracts, and streamlined export controls. The company also recommended more flexible procurement rules to accelerate AI adoption by federal agencies.

Regulatory strategies for US AI

The US companies called for a unified federal approach to AI regulation.

OpenAI proposed a regulatory framework managed by the Department of Commerce, warning that fragmented state-level regulations could drive AI development overseas. The company supported a tiered export control framework, allowing broader access to US-developed AI in democratic countries while restricting it in authoritarian states.

Anthropic called for stricter export controls on AI hardware and training data, warning that even minor improvements in model performance could give China a strategic advantage.

Google focused on copyright and intellectual property rights, stressing that its interpretation of ‘fair use’ is important for AI development. The company warned that overly restrictive copyright rules could disadvantage US AI firms compared to their Chinese competitors.

All three companies stressed the need for faster government adoption of AI. OpenAI recommended removing some existing testing and procurement barriers, while Anthropic supported streamlined procurement processes. Google emphasised the need for improved interoperability in government cloud infrastructure.

See also: The best AI prompt generator: Create perfect AI prompts

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Is America falling behind in the AI race? appeared first on AI News.

That was something Ahmed Bukhamseen wanted to change, and his company Quant set out to transform the property investment market and open up pricing information to buyers, speculators, and investors.

As you might expect, at the core of Quant is data – advertised and realised prices, mapping, details of proposed and ongoing construction, geo-spacial data, imagery, and much more. We spoke to Ahmed at the recent MWC (Mobile World Congress) in Barcelona, to talk about the company’s vision and its journey to date. But first we asked what his chosen data platform of choice was – the basis on which the business runs.

“Our main challenge was to first comply with data protection. So, Saudi implemented new regulation laws about data protection, similar to GDPR, run by the Saudi Data and AI Authority. And for that we needed to have a local host for data protection. So that was our main driver for moving from Azure – Microsoft here in Europe – back to Saudi.”

Saudi Arabia, the Middle East, Africa and the APAC are target markets for Chinese tech giant Huawei, but it wasn’t an immediate given for Quant. “We discovered that Huawei offer cloud services late […] about this time last year. We started comparing Huawei with others.”

As Quant’s business model developed, so too did its requirements. The company started out just supplying information it drew from the Saudi municipal data registries, but has since enriched that core information with local data, and high-res satellite imagery.

“We were using a data centre [here] and the data had to go from Saudi to Europe and back. We were running AI on the edge, on images without storing them. But now we have different use cases, so we needed to store data before and after processing, after enrichment or maximisation. There was a huge cost, and we didn’t expect it at the beginning.”

Part of the issue was the imagery the company was pulling down from satellites to get up-to-date pictures of existing buildings on the ground, and those in the process of construction. It was grabbing detailed images once every two weeks, and is about to go live with a daily satellite photography cadence.

“We started testing Huawei in terms of latency, especially, because we streamed high volume data, and would like the individuals using our mobile application to get a seamless experience, and start navigating in-app.”

The app released by Quant opens the property market to anyone: individuals buying their first place, portfolio managers scoping new possibilities, investors of all sizes, landlords, and those – like Ahmed was once himself – hoping just to find a place to rent without the massive variance in prices that was the norm before Quant began its service.

Quant combines different data sources such a property prices, maps, satellite imagery, and government-approved planning documents among them to give its app users everything they need to find property or land to buy and sell.

The future will see Quant revising its data, further enriching it, and adding value for its clients. “We need to develop specific feature detection or object detection. For example, municipality data could state a company has zoning for a warehouse or extension, and we don’t know which one the owner decided on. But from space, we can build models and see the exact the building plan.”

Although Quant likely has the technical ‘chops’ to build its own models, it’s turning to Huawei:

“With our new development, computing the model [ourselves] would take us six months to deploy. Now when we run it, it takes four hours. And last time I checked with the team, we run it every week,” Ahmed said.

Quant’s plans include a service for retailers that will advise on the best areas to site their stores, given the known demographics of a neighbourhood – all data it collects, collates, enriches, and presents. Plus, there is a project on the table that extends the data scope to take in the rental market.

Given the speed at which Saudi Arabia develops, Quant and delivery partner Huawei need to move at least as fast. “We want to see you in Saudi, and see the return on real estate! We don’t talk about 2% or 5% growth: We’re talking about multiplying investments similar to the Bitcoin market. Foreign investors will be able to buy property or land with just the touch of a button, and they need data for that. They need to know the growth areas, how much it costs in perspective, before making decisions.”

You can download the Quant app, read more about the company on its website, and check out Huawei’s service offerings for Saudi and beyond.

(Image source: European Space Agency, licensed under CC BY-SA 3.0 IGO.)

The post Dream properties at the touch of a button: Quant and Huawei in Saudi Arabia appeared first on AI News.

According to multiple sources familiar with the matter, Apple is in advanced talks to use Alibaba’s Qwen AI models for its iPhone lineup in mainland China. The move would depart from Apple’s global strategy of using OpenAI’s GPT models for its AI features, highlighting the company’s willingness to adapt to local market conditions.

The technical edge of Qwen AI

Qwen AI is attractive to Apple in China because of the former’s proven capabilities in the open-source AI ecosystem. Recent benchmarks from Hugging Face, a leading collaborative machine-learning platform, position Qwen at the forefront of open-source large language models (LLMs).

The platform’s data shows Qwen-powered models dominating the top 10 positions in performance global rankings, demonstrating the technical maturity that Apple seeks for its AI integration.

“The selection of Qwen AI for iPhone integration would validate Alibaba’s AI capabilities,” explains Morningstar’s senior equity analyst Chelsey Lam. “This could be particularly important for Apple’s strategy to re-invigorate iPhone sales in China, where AI features have become increasingly important for smartphone users.”

Regulatory navigation and market impact

The potential partnership reflects an understanding of China’s AI regulatory landscape. While Apple’s global AI features remain unavailable in China due to regulatory requirements, partnering with Alibaba could provide a compliant pathway to introduce advanced AI capabilities.

Market reaction to the news has been notably positive:

• Alibaba’s stock surged 7.6% on Monday, followed by an additional 1.3% gain on Tuesday
• Apple shares responded with a 2.2% increase
• The tech sector has shown renewed interest in China-focused AI integration strategies

Development timeline and expectations

The timing of the potential collaboration aligns with Apple’s upcoming China developer conference in Shanghai, scheduled for March 25. Industry observers speculate the event could serve as a platform on which to announce the integration of Qwen AI features into the iPhone ecosystem.

“The partnership could change how international tech companies approach AI localisation in China,” noted a senior AI researcher at a leading Chinese university, speaking anonymously. “It’s not just about technology integration; it’s about creating a sustainable model for AI development in China’s regulatory framework.”

Implications for developers and users

For Chinese iOS developers, the potential integration of Qwen AI presents opportunity. The partnership could enable:

• Creation of locally optimised AI applications
• Enhanced natural language processing capabilities specific to Chinese users
• Seamless integration with local services and platforms

Prospects and industry impact

The effects of the partnership extend beyond immediate market concerns. As global tech companies navigate operating in China, the Apple-Alibaba collaboration could serve as a blueprint for future integration.

For Alibaba, securing Apple as a flagship partner could catalyse more partnerships with global technology companies seeking AI solutions for China. The collaboration would demonstrate Qwen AI’s capability to meet the stringent requirements of one of the world’s most demanding tech companies.

Looking ahead

While both companies maintain official silence on the partnership, the tech community awaits announcements at the upcoming Shanghai developer conference. The development is important when AI capabilities increasingly influence smartphone purchasing decisions. For Apple, success in China will impact its global growth trajectory, and integrating Qwen AI could provide the competitive edge it needs to maintain its premium market position against local manufacturers offering advanced AI features.

It underscores a broader trend in the tech industry: the growing importance of localised AI solutions in major markets.

See also: Has Huawei outsmarted Apple in the AI race?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here

The post Could Alibaba’s Qwen AI power the next generation of iPhones in China? appeared first on AI News.

Cambricon’s breakthrough into profitability signals a significant shift in the US-China AI chip race, transforming from a 2016 startup into China’s most valuable artificial intelligence company, now valued at approximately 300 billion yuan ($41 billion).

While this represents only a fraction of Nvidia’s $3 trillion market capitalisation, it marks China’s growing capability to develop sophisticated AI chips domestically.

The company’s financial turnaround is particularly noteworthy in the context of technological competition between the world’s two largest economies. After years of losses, Cambricon reported its first quarterly profit in the final quarter of 2024, with net profits ranging from 240 million yuan to 328 million yuan, despite posting a 724 million yuan loss in the first nine months.

The market’s response to this shifting dynamic in the US-China AI chip race has been remarkable. Cambricon’s shares on the Shanghai Stock Exchange’s Star Market have surged more than 470% over the past year, climbing from 120.80 yuan to 695.96 yuan.

The company projects a 70% revenue increase to 1.2 billion yuan in 2024, driven by China’s aggressive buildup of computing infrastructure to support its AI ambitions.

At the technical level, Cambricon has positioned itself as China’s answer to US chip restrictions with its 7-nanometre AI chips. The company’s flagship Cambricon-1A processor has gained significant traction in the domestic market, particularly in products from major technology companies like Huawei Technologies.

The stakes in the US-China AI chip race continue to rise, with analysts at Changjiang Securities projecting that China’s AI semiconductor market will reach 178 billion yuan by 2025. Beijing’s push for semiconductor self-sufficiency and increasing investments from domestic technology companies in AI infrastructure are fueling this growth.

Recent US regulations announced in January 2025 have intensified the race, restricting Chinese access to advanced AI technology and limiting it to American companies and their allies. In response, major Chinese technology companies are investing heavily in domestic computing infrastructure.

ByteDance, TikTok’s parent company, has committed 4.5 billion yuan to a new computing centre in Datong City, Shanxi province. This highlights the growing market opportunity for domestic chip manufacturers.

While Cambricon’s progress represents a significant advancement in the US-China AI chip race, challenges remain. The company must continue to narrow the technological gap with international competitors while maintaining its growth trajectory.

However, supportive government policies and growing domestic demand provide a favourable environment for continued development. Cambricon’s inclusion in the SSE 50 Index, which tracks the Shanghai Stock Exchange’s most valuable companies, underscores its strategic importance to China’s technology sector.

As global tensions persist and access to foreign technology becomes more restricted, developing domestic AI chip capabilities has become increasingly important for China’s technological advancement and economic security.

The post US-China AI chip race: Cambricon’s first profit lands appeared first on AI News.

This breakthrough represents a significant step in China’s ongoing efforts to become self-reliant in AI technology, especially in light of escalating US limitations on access to advanced semiconductors for its competitors.

According to the company’s Institute of AI, one of the models, TeleChat2-115B and another unnamed model were trained on tens of thousands of Chinese-made chips. This achievement is especially noteworthy given the tighter US export rules that have limited China’s ability to purchase high-end processors from Nvidia and other foreign companies. In a statement shared on WeChat, the AI institute claimed that this accomplishment demonstrated China’s capability to independently train LLMs and signals a new era of innovation and self-reliance in AI technology.

The scale of these models is remarkable. China Telecom stated that the unnamed LLM has one trillion parameters. In AI terminology, parameters are the variables that help the model in learning during training. The more parameters there are, the more complicated and powerful the AI becomes.

Chinese companies are striving to keep pace with global leaders in AI based outside the country. Washington’s export restrictions on Nvidia’s latest AI chips such as the A100 and H100, have compelled China to seek alternatives. As a result, Chinese companies have developed their own processors to reduce reliance on Western technologies. For instance, the TeleChat2-115B model has approximately 100 billion parameters, and therefore can perform as well as mainstream platforms.

China Telecom did not specify which company supplied the domestically-designed chips used to train its models. However, as previously discussed on these pages, Huawei’s Ascend chips play a key part in the country’s AI plans.

Huawei, which has faced US penalties in recent years, is also increasing its efforts in the artificial intelligence field. The company has recently started testing its latest AI processor, the Ascend 910C, with potential clients waiting in the domestic market. Large Chinese server companies, as well as internet giants that have previously used Nvidia chips, are apparently testing the new chip’s performance. Huawei’s Ascend processors, as one of the few viable alternatives to Nvidia hardware, are viewed as a key component of China’s strategy that will lessen its reliance on foreign technology.

In addition to Huawei, China Telecom is collaborating with other domestic chipmakers such as Cambricon, a Chinese start-up specialising in AI processors. The partnerships reflect a broader tendency in China’s tech industry to build a homegrown ecosystem of AI solutions, further shielding the country from the effects of US export controls.

By developing its own AI chips and technology, China is gradually reducing its dependence on foreign-made hardware, especially Nvidia’s highly sought-after and therefore expensive GPUs. While US sanctions make it difficult for Chinese companies to obtain the latest Nvidia hardware, a black market for foreign chips has emerged. Rather than risk operating in the grey market, many Chinese companies prefer to purchase lower-powered alternatives such as previous-gen models to maintain access to Nvidia’s official support and services.

China’s achievement reflects a broader shift in its approach to AI and semiconductor technology, emphasising self-sufficiency and resilience in an increasingly competitive global economy and in face of American protectionist trade policies.

(Photo by Mark Kuiper)

See also: Has Huawei outsmarted Apple in the AI race?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post China Telecom trains AI model with 1 trillion parameters on domestic chips appeared first on AI News.

Huawei’s move showcases its ability to innovate despite facing US sanctions, but it also spells uncertainty for Apple’s market share in Mainland China, one of its most crucial markets. Let’s explore why.

Apple and its AI gambit: Worth the hype?

The delay in rolling out AI features has led many industry analysts to question whether Apple’s AI push is ready for prime time. “With lots of words like ‘later this year’ and ‘early next year’, the core Apple message for iPhone 16 was: Next year will be better,” Reuters quoted Needham analyst Laura Martin.

Apple’s suite of tools, collectively known as Apple Intelligence, has been in development for some time. The company has touted the technology since its developer’s conference in June. However, the software won’t be included in the initial versions of the new iPhones, and many key features won’t arrive until next year. 

For now, the technology primarily focuses on summarising messages and notifications rather than matching the more advanced capabilities of rival systems.

Delayed AI features in iPhone 16 dampen enthusiasm–especially in China

In China, the excitement of the launch of the iPhone 16 series on Monday quickly turned to disappointment for many local Apple users when they learned that the AI features would not be immediately available in their language until next year. 

At the same time, Huawei’s AI assistant, with text summary, translation, and editing functions, and AI-boosted image editing functions, such as trimming unwanted parts of photos, will be readily available once Mate XT goes on sale later this month. This delay has prompted scepticism about the value proposition of the new iPhones in China, especially given the fierce competition from local rivals like Huawei.

“The absence of AI in China is akin to cutting one of Apple’s arms,” one commentator remarked on Weibo, China’s popular microblogging platform. Another user pointedly asked, “With the biggest selling point unavailable, shouldn’t you charge us half the price?” These sentiments reflect a growing frustration among Chinese consumers who feel they are not getting the total value of Apple’s latest innovations, according to South China Morning Post.

In contrast, Huawei’s new Mate XT, which users can fold three ways like an accordion screen door, has already received more than four million pre-orders, for which no deposit is required, according to the company’s website. According to research firm IDC, the global market for foldable phones was around 4 million units in the second quarter.

“Today, we bring you a product everyone can think of but could not make. Our team has been working hard for five years and has never given up,” Huawei executive director Richard Yu said at the launch.

On the other hand, Apple has yet to announce an AI partner in China to power Apple Intelligence. This is because the uncertain regulatory landscape in mainland China adds to the complexity of the situation. The country’s Ministry of Industry and Information Technology has approved 188 large language models for public use, none of which are from foreign companies. 

This raises questions about whether Apple’s AI features will be available in mainland China even when rolled out in other Chinese-speaking regions. Apple has stated on its website that the launch date of its AI feature in China will depend on the decision of Chinese regulators. 

But Apple has to catch up, and it has to be fast. Apple sales have been dwindling, and the company’s ranking in the world’s second-largest economy has dropped from third to sixth place. This is even though Apple has long experienced robust demand in China, where new iPhone debuts have historically caused a frenzy. 

The industry took a surprising turn when Shenzhen-based Huawei returned to the high-end smartphone sector last year with the debut of a device powered by a domestically manufactured chip. This defied US sanctions that had cut off access to the global chipset supply chain. 

The Mate 60 Pro’s debut startled analysts and US officials. In addition, Huawei started offering two-way foldable phones in its inventory, and strong sales in China helped it beat Samsung Electronics as the world’s largest vendor of such phones earlier this year. 

The delay with Apple Intelligence in China will give competitors like Huawei a window of opportunity to capture market share and establish themselves as leaders in AI-powered smartphones within their home turf. This could make it more challenging for Apple to regain ground once its AI features finally arrive in the country.

Ultimately, Apple’s AI ambitions in China represent a high-stakes gamble for the tech giant. While the company’s brand still holds significant appeal, the delayed rollout of AI features and intense competition from resurgent local players like Huawei pose serious challenges. 

As the smartphone market in China continues to evolve, Apple’s ability to adapt its AI strategy to local conditions may determine its future success in this critical market.

(Photo by James Lee)

See also: Could an Apple-Meta partnership redefine the AI landscape?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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IDC’s senior research director, Nabila Popal, noted that Android devices are expected to see growth rates nine times faster than iOS at 7.1% compared to 0.8%. This disparity is attributed to increasing competition for Apple in China and a higher comparison base year for the iPhone maker.

However, the forecast suggests potential upside for iOS, depending on the reception of GenAI features in the upcoming iPhone 16 and Apple’s ability to establish local AI partnerships in China. IDC anticipates an improved trajectory for iOS in 2025, with 4% YoY growth expected due to the anticipated momentum of Apple Intelligence.

The report highlights the rapid adoption of GenAI smartphones, forecasting a 344% growth in this segment to capture 18% of the total market by the end of 2024. IDC research director Anthony Scarsella pointed out that most flagship models will likely incorporate some on-device GenAI features.

“Although GenAI smartphones are relatively new in the market, premium flagships will continue to adopt GenAI features to help separate them from the pack,” said Anthony Scarsella, research director for IDC’s Worldwide Quarterly Mobile Phone Tracker. Notably, the average selling price for GenAI-capable smartphones is expected to be more than double that of non-GenAI devices, further driving the premiumisation trend in the industry.

The smartphone market’s recovery comes after a challenging period. IDC’s data shows 12% growth in the first quarter of 2024, followed by 9% growth in the second quarter. This positive trend has bolstered confidence in the industry’s performance for the latter half of the year.

Even the latest Canalys research revealed that the worldwide smartphone market grew strongly in the second quarter of 2024, with shipments reaching 288.9 million units. “The market has grown for three consecutive quarters, driven by product innovation initiatives and improvements in business conditions,” Canalys stated.

Mirroring IDC’s data, Canalys noted that despite a modest 1% shipment increase, Samsung held onto pole position with 53.5 million units shipped. Meanwhile, Apple held second place with 45.6 million units, bolstered by solid momentum in North America and APAC’s emerging markets. 

China’s Xiaomi, with its competitive product offerings, followed closely with shipments of 42.3 million units to achieve a market share of 15%. “Greater China’s growth trajectory now mirrors global markets, driven by improved macroeconomic conditions and renewed business confidence among channel and smartphone brands,” the report stated.

Like elsewhere, the Chinese domestic market will see local Chinese brands use GenAI features to stand out in the premium segment, aiming to capture rising high-end spending and apply successful strategies worldwide. 

“Samsung will inevitably focus on integrating its Galaxy ecosystem to create strong value propositions for consumers via its flagship offerings with exclusive GenAI features,” Canalys Senior Analyst Sanyam Chaurasia said. Apple will look to accelerate replacement demand in these markets via its AI strategy, with hybrid models, enhanced privacy and personalised Siri features.” 

Even Canalys doesn’t foresee the smartphone market seeing double-digit growth in 2024 despite the seasonal demand in the year’s second half. Overall, the global technology market analyst firm anticipates the smartphone market to grow in the mid-single digits in 2024, driven by recovering inventory levels, eased import restrictions, and a better economic climate. 

(Photo by Samuel Angor)

See also: Arm unveils new AI designs and software for smartphones

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post IDC: GenAI smartphones to drive global shipment growth in 2024 appeared first on AI News.

However, a congressional spokesperson, who provided this information, chose to remain anonymous due to internal committee policy, as reported by Reuters.

The order was given directly by the UAE’s ambassador to the US, who halted the meetings between staffers from the House Select Committee on China and G42, as well as various Emirati government officials. This development only adds fuel to the fire of high tensions surrounding the scrutiny of G42 amid a $1.5 billion agreement with Microsoft. Some US congresspeople are already worried about sensitive technology getting into the hands of a UAE firm that reportedly has Chinese ties.

The committee’s spokesperson expressed increased concerns regarding the G42-Microsoft deal due to the UAE’s unwillingness to engage in talks. “Expect Congress to become more involved in overseeing these negotiations,” the spokesperson said.

The cancelled meetings may signal a diplomatic crisis due to the increased attention of China hawks in Congress. The efforts of these lawmakers to closely scrutinise the G42-Microsoft deal have particularly sparked controversies. Members of Congress are focused on ensuring that sensitive AI developments and products resulting from the agreement will not be diverted by the Emiratis to China.

The State Department gave no comment, whereas G42 directed the media to the Emirati government. The UAE embassy spokesperson announced that the situation resulted from a “miscommunication,” as they were notified of the staff delegation just the day before their planned arrival. The embassy emphasised its regular engagement with committee members and staffers in recent months, asserting that the committee has been kept informed about joint UAE-US efforts to strengthen control over critical advanced technologies.

The congressional staffers had planned these meetings as part of a regional visit from July 16-19. Their agenda included discussions on the transfer of sophisticated chips from companies like Nvidia to the UAE and Saudi Arabia, as well as US-China tech competition.

Ambassador Yousef Al Otaiba cited a July 11 letter from committee chairman John Moolenaar to US National Security Advisor Jake Sullivan as the reason for the cancellations. This letter, co-signed by House Foreign Affairs chair Michael McCaul, requested a White House intelligence briefing on Microsoft’s investment in G42 before the deal could progress to its second phase, which would involve transferring export-restricted semiconductor chips from Nvidia and sophisticated AI model weights.

The Biden administration has taken a positive view of the G42-Microsoft deal, stating that G42’s severance from China’s Huawei has been a major positive factor for the deal. However, last year, the administration also imposed sweeping curbs on AI chip exports, requiring licenses for shipments under a more restrictive policy than the previous Trump administration. Additionally, the policy of restricting exports to China requires licenses for exports to the UAE and some other Middle Eastern countries.

It is noted that a regional visit by a congressional delegation took place, during which they met with Saudi officials who expressed a desire to alleviate US companies’ concerns about the activities of the Chinese government in Saudi Arabia. Their goal was to obtain permission to import advanced American chips.

The level of interaction between US and other countries’ authorities illustrates the link between technological innovation, international political relationships, and national security issues.

See also: UAE unveils new AI model to rival big tech giants

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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The third largest cloud services company in China, following Alibaba and Huawei, Tencent recently revealed the three chips at its 2021 Digital Ecology Conference.

Current information on the three chips can be summarised as follows:

• Zixiao – an “AI reasoning” chip that supposedly offers 100 percent better performance than rival products. It combines image and video processing with natural language processing, search recommendations, and other features
• Xuangling – a SmartNIC or Data Processing Unit (DPU) that runs virtualisation of storage and networking for a cloud host’s CPU so that it doesn’t have to. Tencent claims this comes with zero cost to the host CPU and that it performs four times faster than similar industry products
• Canghai – a video transcoding chip that supposedly delivers a 30 percent improved compression rate over other on-market products. It achieves this through multi-core expansion architecture, a high-performance coding pipeline, and a hierarchical memory layout

Whilst these suggested improvements are substantial, the reliability of these figures cannot yet be accounted for.

Their development comes on the back of Tencent establishing a chip research and development lab in Penglai in 2020. Its goal of achieving full end-to-end coverage of Tencent’s chip design and verification appears to have been realised with the announcement.

Tang Daosheng, senior executive VP of Tencent, said at the conference: “Facing strong business needs, Tencent developed a long-term chip research and development investment plan. Currently, it has already implemented three directions with substantial progress.”

Tencent currently operates outside of Asia in the USA, Brazil, Germany, and Russia, with keen plans to expand further into Europe, the Americas, and Africa.

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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