In the Chinese market, demand for Apple’s iPhone continues to falter, with Bloomberg reporting a significant 33% drop in iPhone shipments in February compared to the same month last year. As per Bloomberg’s report on March 26th, despite China remains Apple’s most crucial overseas market, iPhone demand in China has remained consistently low.

According to official data cited in the report, iPhone shipments in the Chinese market have plummeted by 33% in February compared to the same period last year, marking the second consecutive month of decline. Influenced by the later timing of the Lunar New Year compared to 2023, overseas smartphone shipments in February were said to be only around 2.4 million units, with iPhones accounting for a significant portion.

Apple is reportedly the only overseas manufacturer with a substantial market share in China. Data from the China Academy of Information and Communications Technology (CAICT) shows that iPhone shipments in January were approximately 5.5 million units, marking a significant 39% decline compared to the same month last year.

Still, a report from The Wall Street Journal on March 22 has highlighted that intensified competition with Chinese manufacturers like Huawei has led to tough sales competition for Apple’s iPhone in China.

Thus, the same report from Wall Street Journal indicated that Apple is in talks with Baidu to potentially integrate Baidu’s generative AI services into its own products, including iPhones, for the Chinese market. While the agreement is still in its early stages, incorporating a Chinese version of AI could potentially give Apple a competitive edge in the Chinese market.

Apple is reportedly intensifying its AI efforts, with plans to collaborate with Baidu on the Chinese version of its iPhone 16 series this year, as per a report from Economic Daily News. The devices will feature Baidu’s developed Generative AI technology, thus sparking a new wave of AI server deployment by Baidu.

In China, due to official requirements, providing generative AI services to ordinary consumers requires prior approval and scrutiny from relevant authorities. Only Chinese companies like Baidu and Alibaba have been granted permission. Apple has chosen Baidu as its AI service partner in the Chinese market.

According to the aforementioned reports, Apple and Baidu have entered into negotiations, planning to integrate Baidu’s generative AI services into products such as the iPhone for sale in China. By incorporating a Chinese version of AI, Apple aims to enhance its competitive advantage in the Chinese market.

Inventec, a Taiwanese manufacturer, has had close collaboration with Baidu for 13 years in the field of customized server manufacturing, has jointly developed an AI computing platform. Serving as a key server manufacturing partner for Baidu’s “All in AI” strategy, Inventec plays a major role in Apple’s swift push into the Chinese AI market alongside Baidu.

Inventec has traditionally refrained from commenting on order dynamics and customer relationships, emphasizing instead the robust shipment momentum of AI servers this year. Shipments are expected to more than double compared to last year. With AI server revenue accounting for approximately 5% to 6% last year, it is projected to surpass 10% this year.

Sources cited by the same report from Economic Daily News are optimistic that with Baidu becoming a key partner for Apple in the China’s generative AI landscape, Inventec’s AI server shipment momentum is poised to amplify, benefiting from the opportunities brought by the collaboration between Apple and Baidu.

As per a previous report from TrendForce, Baidu’s foray into AI chips can be traced back to as early as 2011. After seven years of development, Baidu officially unveiled its self-developed AI chip, Kunlun 1, in 2018. Built on a 14nm process and utilizing the self-developed XPU architecture, Kunlun 1 entered mass production in 2020. It is primarily employed in Baidu’s search engine and Xiaodu businesses.

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(Photo credit: Apple)

On October 17th, the U.S. Department of Commerce announced an expansion of export control, tightening further restrictions. In addition to the previously restricted products like NVIDIA A100, H100, and AMD MI200 series, the updated measures now include a broader range, encompassing NVIDA A800, H800, L40S, L40, L42, AMD MI300 series, Intel Gaudi 2/3, and more, hindering their import into China. This move is expected to hasten the adoption of domestically developed chips by Chinese communications service providers (CSPs).

TrendForce’s Insights:

1. Chinese CSPs Strategically Invest in Both In-House Chip Development and Related Companies

In terms of the in-house chip development strategy of Chinese CSPs, Baidu announced the completion of tape out for the first generation Kunlun Chip in 2019, utilizing the XPU. It entered mass production in early 2020, with the second generation in production by 2021, boasting a 2-3 times performance improvement. The third generation is expected to be released in 2024. Aside from independent R&D, Baidu has invested in related companies like Nebula-Matrix, Phytium, Smartnvy, and. In March 2021, Baidu also established Kunlunxin through the split of its AI chip business.

Alibaba, in April 2018, fully acquired Chinese CPU IP supplier C-Sky and established T-head semiconductor in September of the same year. Their first self-developed chip, Hanguang 800, was launched in September 2020. Alibaba also invested in Chinese memory giant CXMT, AI IC design companies Vastaitech, Cambricon and others.

Tencent initially adopted an investment strategy, investing in Chinese AI chip company Enflame Tech in 2018. In 2020, it established Tencent Cloud and Smart Industries Group(CSIG), focusing on IC design and R&D. In November 2021, Tencent introduced AI inference chip Zixiao, utilizing 2.5D packaging for image and video processing, natural language processing, and search recommendation.

Huawei’s Hisilicon unveiled Ascend 910 in August 2019, accompanied by the AI open-source computing framework MindSpore. However, due to being included in the U.S. entity list, Ascend 910 faced production restrictions. In August 2023, iFLYTEK, a Chinese tech company, jointly introduced the “StarDesk AI Workstation” with Huawei, featuring the new AI chip Ascend 910B. This is likely manufactured using SMIC’s N+2 process, signifying Huawei’s return to self-developed AI chips.

2. Some Chinese Companies Turn to Purchasing Huawei’s Ascend 910B, Yet It Lags Behind A800

Huawei’s AI chips are not solely for internal use but are also sold to other Chinese companies. Baidu reportedly ordered 1,600 Ascend 910B chips from Huawei in August, valued at approximately 450 million RMB, to be used in 200 Baidu data center servers. The delivery is expected to be completed by the end of 2023, with over 60% of orders delivered as of October. This indicates Huawei’s capability to sell AI chips to other Chinese companies.

Huawei’s Ascend 910B, expected to be released in the second half of 2024, boasts hardware figures comparable to NVIDIA A800. According to tests conducted by Chinese companies, its performance is around 80% of A800. However, in terms of software ecosystem, Huawei still faces a significant gap compared to NVIDIA.

Overall, using Ascend 910B for AI training may be less efficient than A800. Yet with the tightening U.S. policies, Chinese companies are compelled to turn to Ascend 910B. As user adoption increases, Huawei’s ecosystem is expected to improve gradually, leading more Chinese companies to adopt its AI chips. Nevertheless, this will be a protracted process.

The United States has elevated its efforts to curtail the advancement of high-end chips in China. As reported by the CLS News, various companies within China have indicated they received advance notifications and have already amassed chip stockpiles. Analysts suggest that this new wave of bans implies a further restriction by the U.S. on China’s computational capabilities, making the development of domestically-manufactured GPUs in China a matter of utmost importance.

According to the latest regulations, chips, including Nvidia’s A800 and H800, will be impacted by the export ban to China. An insider from a Chinese server company revealed they received the ban notice at the beginning of October and have already stockpiled a sufficient quantity. Nevertheless, they anticipate substantial pressure in the near future. The procurement manager for a downstream customer of Inspur noted that they had proactively shared this information and urged potential buyers to act promptly if they require related products.

Larger companies like Tencent and Baidu are less affected by the ban due to their ample stockpiles. On October 17th, HiRain Technologies announced that its subsidiary had purchased 75 units of H800 and 22 units of A800 from supplier A and had resolved this issue two weeks ago.

(Image: NVIDIA)

OpenAI’s ChapGPT, Microsoft’s Copilot, Google’s Bard, and latest Elon Musk’s TruthGPT – what will be the next buzzword for AI? In just under six months, the AI competition has heated up, stirring up ripples in the once-calm AI server market, as AI-generated content (AIGC) models take center stage.

The convenience unprecedentedly brought by AIGC has attracted a massive number of users, with OpenAI’s mainstream model, GPT-3, receiving up to 25 million daily visits, often resulting in server overload and disconnection issues.

Given the evolution of these models has led to an increase in training parameters and data volume, making computational power even more scarce, OpenAI has reluctantly adopted measures such as paid access and traffic restriction to stabilize the server load.

High-end Cloud Computing is gaining momentum

According to Trendforce, AI servers currently have a merely 1% penetration rate in global data centers, which is far from sufficient to cope with the surge in data demand from the usage side. Therefore, besides optimizing software to reduce computational load, increasing the number of high-end AI servers in hardware will be another crucial solution.

Take GPT-3 for instance. The model requires at least 4,750 AI servers with 8 GPUs for each, and every similarly large language model like ChatGPT will need 3,125 to 5,000 units. Considering ChapGPT and Microsoft’s other applications as a whole, the need for AI servers is estimated to reach some 25,000 units in order to meet the basic computing power.

As the emerging applications of AIGC and its vast commercial potential have both revealed the technical roadmap moving forward, it also shed light on the bottlenecks in the supply chain.

The down-to-earth problem: cost

Compared to general-purpose servers that use CPUs as their main computational power, AI servers heavily rely on GPUs, and DGX A100 and H100, with computational performance up to 5 PetaFLOPS, serve as primary AI server computing power. Given that GPU costs account for over 70% of server costs, the increase in the adoption of high-end GPUs has made the architecture more expansive.

Moreover, a significant amount of data transmission occurs during the operation, which drives up the demand for DDR5 and High Bandwidth Memory (HBM). The high power consumption generated during operation also promotes the upgrade of components such as PCBs and cooling systems, which further raises the overall cost.

Not to mention the technical hurdles posed by the complex design architecture – for example, a new approach for heterogeneous computing architecture is urgently required to enhance the overall computing efficiency.

The high cost and complexity of AI servers has inevitably limited their development to only large manufacturers. Two leading companies, HPE and Dell, have taken different strategies to enter the market:

• HPE has continuously strengthened its cooperation with Google and plans to convert all products to service form by 2022. It also acquired startup Pachyderm in January 2023 to launch cloud-based supercomputing services, making it easier to train and develop large models.
• In March 2023, Dell launched its latest PowerEdge series servers, which offers options equipped with NVIDIA H100 or A100 Tensor Core GPUs and NVIDIA AI Enterprise. They use the 4th generation Intel Xeon Scalable processor and introduce Dell software Smart Flow, catering to different demands such as data centers, large public clouds, AI, and edge computing.

With the booming market for AIGC applications, we seem to be one step closer to a future metaverse centered around fully virtualized content. However, it remains unclear whether the hardware infrastructure can keep up with the surge in demand. This persistent challenge will continue to test the capabilities of cloud server manufacturers to balance cost and performance.

(Photo credit: Google)