According to a Reuters, despite the U.S. expanding export controls on advanced artificial intelligence (AI) chips to China last year, Chinese universities and research institutions have recently acquired high-end AI chips from Nvidia through distributors.

Reviewing hundreds of bidding documents, Reuters found that since the U.S. expanded chip export controls on November 17 last year, ten Chinese entities have acquired Nvidia’s advanced chips embedded in server products produced by U.S. firms Supermicro, Dell, and Taiwanese company Gigabyte Technology.

Based on this Reuters report, bidding documents not reported from November 20 last year to February 28 this year show that Chinese institutions such as the Chinese Academy of Sciences, Shandong Artificial Intelligence Institute, Hubei Earthquake Administration, Shandong University, Southwest University, a technology investment company owned by the Heilongjiang Provincial Government, a state-owned aerospace research center, and a space science center have purchased these server products from distributors, which include some of Nvidia’s most advanced chips.

In response, a Nvidia spokesperson told Reuters that the products involved in these bids were exported before the ban was implemented in the United States. The spokesperson stated that the report does not imply that Nvidia or any of its partners violated export control regulations, and the proportion of these products in global sales is negligible. Nvidia complies with U.S. regulatory standards.

Both Supermicro and Dell stated that they would investigate and take action if any third-party illegal exports or re-exports are found. Gigabyte, the Taiwanese company mentioned in the report, told the Central News Agency that it has fully complied with relevant regulations since the chip ban took effect on November 17 last year, and has not shipped any restricted products to China. Gigabyte reiterated its strict adherence to relevant Taiwanese laws and international embargo regulations, stating that there has been no violation of any embargo regulations.

In 2023, the United States further restricted Chinese businesses from acquiring high-end AI chips. At that time, Nvidia responded by launching a China-specific version, the H20. TrendForce also presented relevant data for the Chinese market, indicating that Chinese CSP companies, including ByteDance, Baidu, Alibaba, and Tencent (BBAT), accounted for approximately 6.3% of high-end AI server shipments in 2023. Considering the ban and subsequent risks, it is estimated that the proportion in 2024 may be less than 4%.

(Photo credit: NVIDIA)

As per Chinese media mydrivers’ report, it has indicated that NVIDIA updated the EULA terms in the CUDA 11.6 installer, explicitly prohibiting third-party GPU companies from seamlessly integrating CUDA software. This move has reportedly stirred up discussions among the market.

NVIDIA updated the EULA agreement for CUDA 11.6, explicitly stating, “You may not reverse engineer, decompile or disassemble any portion of the output generated using SDK elements for the purpose of translating such output artifacts to target a non-NVIDIA platform.”

This move is reportedly speculated to target third-party projects like ZLUDA, involving Intel and AMD, as well as compatibility solutions from Chinese firms like Denglin Technology and MetaX Technology.

In response, MooreThreads issued an official statement, confirming that its MUSA and MUSIFY technologies remain unaffected. MUSA and MUSIFY are not subject to NVIDIA EULA terms, ensuring developers can use them with confidence.

In fact, since 2021, NVIDIA has prohibited other hardware platforms from running CUDA software analog layers but only warned about it in the online EULA user agreement. Currently, NVIDIA has not explicitly named any parties, issuing warnings in the agreement without taking further action, although further measures cannot be ruled out in the future.

In the past, CUDA development and its ecosystem were widely regarded as NVIDIA’s moat. However, processor architect Jim Keller previously criticized CUDA on the X platform, stating, “CUDA is a swamp, not a moat,” adding, “CUDA is not beautiful. It was built by piling on one thing at a time.”

Meanwhile, according to the account rgznai100 posting on Chinese blog CSDN, NVIDIA’s actions will have a significant impact on AI chip/GPGPU companies that previously adopted CUDA-compatible solutions. NVIDIA may initially resort to legal measures, such as lawsuits, against GPGPU companies following similar paths.

Therefore, Chinese enterprises should endeavor to enhance collaboration with the open-source community to establish an open AI compilation ecosystem, reducing the risks posed by NVIDIA’s market dominance.

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(Photo credit: NVIDIA)

According to the Economic Daily News, the AI wave is ushering in a demand for updated specifications in CMOS Image Sensors (CIS), with the global CIS leader, Sony Corporation, aggressively positioning itself to take advantage of this trend. As part of the semiconductor industry’s move towards localized production, Sony has placed significant orders with TSMC’s new Kumamoto plant in Japan, boosting the production volume for the fourth quarter and rapidly increasing the new plant’s capacity utilization.

TSMC does not comment on individual customers or orders. Industry sources point out that the CIS component market previously faced an inventory adjustment issue for over a year. Recently, with clients restarting stock replenishment in anticipation of a recovery, coupled with the AI effect, various end-use applications are adopting lenses developed specifically for AI applications. This shift is expected to drive a new wave of demand for replacing old lenses with new ones to capitalize on AI lens opportunities.

Sony is optimistic about future opportunities in automotive and consumer sectors, and intends to extensively utilize TSMC’s 22nm process for producing CIS components and Image Signal Processors.

Furthermore, to seize the AI business opportunities, Sony has launched Digital Signal Processors (DSP) equipped with AI algorithms, which are expected to enhance applications such as human motion analysis, image processing enhancement, or human tracking. Especially with Sony securing large orders from clients, it is poised to become a major product line in the AI era.

TSMC’s new Kumamoto plant in Japan recently opened and is in the equipment installation phase, with production expected to start as early as the fourth quarter, focusing on 40, 28/22 nm processes for automotive and industrial clients.

The joint venture company for TSMC’s Japan plant, JASM, includes Sony as the largest shareholder besides TSMC. Sony has been a major client of TSMC for outsourced wafer production for many years. With the Kumamoto plant set to start production by the end of the year, Sony is almost certain to secure a significant share of wafer capacity, becoming a major client that fills the capacity utilization rate of TSMC’s Kumamoto plant.

(Image: TSMC)

The previously elusive NVIDIA data center GPU, H100, has seen a noticeable reduction in delivery lead times amid improved market supply conditions, as per a report from Tom’s Hardware. As a result, customers who previously purchased large quantities of H100 chips are reportedly starting to resell them.

The report further points out that the previously high-demand H100 data center GPU, driven by the surge in artificial intelligence applications, has seen a reduction in delivery wait times from a peak of 8-11 months to 3-4 months, indicating a relief in supply pressure.

Additionally, with major cloud providers such as AWS, Google Cloud, and Microsoft Azure offering easier access to AI computing services for customers, enterprises that previously purchased large quantities of H100 GPUs have begun further reselling these GPUs.

For instance, AWS introduced a new service allowing customers to rent GPUs for shorter periods, resolving previous chip demand issues and shortening the waiting time for artificial intelligence chips.

The report also indicates that customers are reselling these GPUs due to reduced scarcity and the high maintenance costs, leading these enterprise customers to make such decisions. This situation contrasts starkly with the market shortage a year ago.

However, even though the current difficulty in obtaining H100 GPUs has significantly decreased, the artificial intelligence market remains robust overall. The demand for large-scale artificial intelligence model computations persists for some enterprises, keeping the overall demand greater than the supply, thereby preventing a significant drop in the price of H100 GPUs.

The report emphasizes that the current ease of purchasing H100 GPUs has also brought about some changes in the market. Customers now prioritize price and practicality when leasing AI computing services from cloud service providers.

Additionally, alternatives to the H100 GPU have emerged in the current market, offering comparable performance and software support but at potentially more affordable prices, potentially contributing to a more equitable market condition.

TrendForce’s newest projections spotlight a 2024 landscape where demand for high-end AI servers—powered by NVIDIA, AMD, or other top-tier ASIC chips—will be heavily influenced by North America’s cloud service powerhouses.

Microsoft (20.2%), Google (16.6%), AWS (16%), and Meta (10.8%) are predicted to collectively command over 60% of global demand, with NVIDIA GPU-based servers leading the charge.

However, NVIDIA still faces ongoing hurdles in development as it contends with US restrictions.

TrendForce has pointed out that, despite NVIDIA’s stronghold in the data center sector—thanks to its GPU servers capturing up to 70% of the AI market—challenges continue to loom.

Three major challenges are set to limit the company’s future growth: Firstly, the US ban on technological exports has spurred China toward self-reliance in AI chips, with Huawei emerging as a noteworthy adversary. NVIDIA’s China-specific solutions, like the H20 series, might not match the cost-effectiveness of its flagship models, potentially dampening its market dominance.

Secondly, the trend toward proprietary ASIC development among US cloud behemoths, including Google, AWS, Microsoft, and Meta, is expanding annually due to scale and cost considerations.

Lastly, AMD presents competitive pressure with its cost-effective strategy, offering products at just 60–70% of the prices of comparable NVIDIA models. This allows AMD to penetrate the market more aggressively, especially with flagship clients. Microsoft is expected to be the most enthusiastic adopter of AMD’s high-end GPU MI300 solutions in 2024.

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(Photo credit: NVIDIA)

Under the formidable impetus of AI, global enterprises are vigorously strategizing for AI chip development, and China is no exception. Who are the prominent AI chip manufacturers in China presently? How do they compare with industry giants like NVIDIA, and what are their unique advantages? A report from TechNews has compiled an overview of eight Chinese AI chip manufacturers in self-development.

• An Overview of AI Chips

In broad terms, AI chips refer to semiconductor chips capable of running AI algorithms. However, in the industry’s typical usage, AI chips specifically denote chips designed with specialized acceleration for AI algorithms, capable of handling large-scale computational tasks in AI applications. Under this concept, AI chips are also referred to as accelerator cards.

Technically, AI chips are mainly classified into three categories: GPU, FPGA, and ASIC. In terms of functionality, AI chips encompass two main types: training and inference. Regarding application scenarios, AI chips can be categorized into server-side and mobile-side, or cloud, edge, and terminal.

The global AI chip market is currently dominated by Western giants, with NVIDIA leading the pack. Industry sources cited by TechNews have revealed data that NVIDIA nearly monopolizes the AI chip market with an 80% market share.

China’s AI industry started relatively late, but in recent years, amid the US-China rivalry and strong support from Chinese policies, Chinese AI chip design companies have gradually gained prominence. They have demonstrated relatively outstanding performance in terminal and large model inference.

However, compared to global giants, they still have significant ground to cover, especially in the higher-threshold GPU and large model training segments.

GPUs are general-purpose chips, currently dominating the usage in the AI chip market. General-purpose GPU computing power is widely employed in artificial intelligence model training and inference fields. Presently, NVIDIA and AMD dominate the GPU market, while Chinese representative companies include Hygon Information Technology, Jingjia Micro, and Enflame Technology.

FPGAs are semi-customized chips known for low latency and short development cycles. Compared to GPUs, they are suitable for multi-instruction, single-data flow analysis, but not for complex algorithm computations. They are mainly used in the inference stage of deep learning algorithms. Frontrunners in this field include Xilinx and Intel in the US, with Chinese representatives including Baidu Kunlunxin and DeePhi.

ASICs are fully customized AI chips with advantages in power consumption, reliability, and integration. Mainstream products include TPU, NPU, VPU, and BPU. Global leading companies include Google and Intel, while China’s representatives include Huawei, Alibaba, Cambricon Technologies, and Horizon Robotics.

In recent years, China has actively invested in the field of self-developed AI chips. Major companies such as Baidu, Alibaba, Tencent, and Huawei have accelerated the development of their own AI chips, and numerous AI chip companies continue to emerge.

Below is an overview of the progress of 8 Chinese AI chip manufacturers:

1. Baidu Kunlunxin

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.

In August of the same year, Baidu announced the mass production of its second-generation self-developed AI chip, Kunlun 2. It adopts a 7nm process and integrates the self-developed second-generation XPU architecture, delivering a performance improvement of 2-3 times compared to the first generation. It also exhibits significant enhancements in versatility and ease of use.

The first two generations of Baidu Kunlunxin products have already been deployed in tens of thousands of units. The third-generation product is expected to be unveiled at the Baidu Create AI Developer Conference scheduled for April 2024.

2. T-Head (Alibaba)

Established in September 2018, T-Head is the semiconductor chip business entity fully owned by Alibaba. It provides a series of products, covering data center chips, IoT chips, processor IP licensing, and more, achieving complete coverage across the chip design chain.

In terms of AI chip deployment, T-Head introduced its first high-performance artificial intelligence inference chip, the HanGuang 800, in September 2019. It is based on a 12nm process and features a proprietary architecture.

In August 2023, Alibaba’s T-Head unveiled its first self-developed RISC-V AI platform, supporting over 170 mainstream AI models, thereby propelling RISC-V into the era of high-performance AI applications.

Simultaneously, T-Head announced the new upgrade of its XuanTie processor C920, which can accelerate GEMM (General Matrix Multiplication) calculations 15 times faster than the Vector scheme.

In November 2023, T-Head introduced three new processors on the XuanTie RISC-V platform (C920, C907, R910). These processors significantly enhance acceleration computing capabilities, security, and real-time performance, poised to accelerate the widespread commercial deployment of RISC-V in scenarios and domains such as autonomous driving, artificial intelligence, enterprise-grade SSD, and network communication.

3. Tencent 

In November 2021, Tencent announced substantial progress in three chip designs: Zixiao for AI computing, Canghai for image processing, and Xuanling for high-performance networking.

Zixiao has successfully undergone trial production and has been activated. Reportedly, Zixiao employs in-house storage-computing architecture and proprietary acceleration modules, delivering up to 3 times the computing acceleration performance and over 45% cost savings overall.

Zixiao chips are intended for internal use by Tencent and are not available for external sales. Tencent profits by renting out computing power through its cloud services.

Recently, according to sources cited by TechNews, Tencent is considering using Zixiao V1 as an alternative to the NVIDIA A10 chip for AI image and voice recognition applications. Additionally, Tencent is planning to launch the Zixiao V2 Pro chip optimized for AI training to replace the NVIDIA L40S chip in the future.

4. Huawei

Huawei unveiled its Huawei AI strategy and all-scenario AI solutions at the 2018 Huawei Connect Conference. Additionally, it introduced two new AI chips: the Ascend 910 and the Ascend 310. Both chips are based on Huawei’s self-developed Da Vinci architecture.

The Ascend 910, designed for training, utilizes a 7nm process and boasts computational density that is said to surpass the NVIDIA Tesla V100 and Google TPU v3.

On the other hand, the Ascend 310 belongs to the Ascend-mini series and is Huawei’s first commercial AI SoC, catering to low-power consumption areas such as edge computing.

Based on the Ascend 910 and Ascend 310 AI chips, Huawei has introduced the Atlas AI computing solution. As per the Huawei Ascend community, the Atlas 300T product line includes three models corresponding to the Ascend 910A, 910B, and 910 Pro B.

Among them, the 910 Pro B has already secured orders for at least 5,000 units from major clients in 2023, with delivery expected in 2024. Sources cited by the TechNews report indicate that the capabilities of the Huawei Ascend 910B chip are now comparable to those of the NVIDIA A100.

Due to the soaring demand for China-produced AI chips like the Huawei Ascend 910B in China, Reuters recently reported that Huawei plans to prioritize the production of the Ascend 910B. This move could potentially impact the production capacity of the Kirin 9000s chips, which are expected to be used in the Mate 60 series.

5. Cambricon Technologies

Founded in 2016, Cambricon Technologies focuses on the research and technological innovation of artificial intelligence chip products.

Since its establishment, Cambricon has launched multiple chip products covering terminal, cloud, and edge computing fields. Among them, the MLU 290 intelligent chip is Cambricon’s first training chip, utilizing TSMC’s 7nm advanced process and integrating 46 billion transistors. It supports the MLUv02 expansion architecture, offering comprehensive support for AI training, inference, or hybrid artificial intelligence computing acceleration tasks.

The Cambricon MLU 370 is the company’s flagship product, utilizing a 7nm manufacturing process and supporting both inference and training tasks. Additionally, the MLU 370 is Cambricon’s first AI chip to adopt chiplet technology, integrating 39 billion transistors, with a maximum computing power of up to 256TOPS (INT8).

6. Biren Technology 

Established in 2019, Biren Technology initially focuses on general smart computing in the cloud.

It aims to surpass existing solutions gradually in various fields such as artificial intelligence training, inference, and graphic rendering, thereby achieving a breakthrough in China’s produced high-end general smart computing chips.

In 2021, Biren Technology’s first general GPU, the BR100 series, entered trial production. The BR100 was officially released in August 2022.

Reportedly, the BR100 series is developed based on Biren Technology’s independently chip architecture and utilizes mature 7nm manufacturing processes.

7. Horizon Robotics 

Founded in July 2015, Horizon Robotics is a provider of smart driving computing solutions in China. It has launched various AI chips, notably the Sunrise and Journey series. The Sunrise series focuses on the AIoT market, while the Journey series is designed for smart driving applications.

Currently, the Sunrise series has advanced to its third generation, comprising the Sunrise 3M and Sunrise 3E models, catering to the high-end and low-end markets, respectively.

In terms of performance, the Sunrise 3 achieves an equivalent standard computing power of 5 TOPS while consuming only 2.5W of power, representing a significant upgrade from the previous generation.

The Journey series has now iterated to its fifth generation. The Journey 5 chip was released in 2021, with global mass production starting in September 2022. Each chip in the series boasts a maximum AI computing power of up to 128 TOPS.

In November 2023, Horizon Robotics announced that the Journey 6 series will be officially unveiled in April 2024, with the first batch of mass-produced vehicle deliveries scheduled for the fourth quarter of 2024.

Several automotive companies, including BYD, GAC Group, Volkswagen Group’s software company CARIAD, Bosch, among others, have reportedly entered into cooperative agreements with Horizon Robotics.

8. Enflame Technology

Enflame Technology, established in March 2018, specializes in cloud and edge computing in the field of artificial intelligence.

Over the past five years, it has developed two product lines focusing on cloud training and cloud inference. In September 2023, Enflame Technology announced the completion of Series D funding round of CNY 2 billion.

In addition, according to reports cited by TechNews, Enflame Technology’s third-generation AI chip products are set to hit the market in early 2024.

Conclusion

Looking ahead, the industry remains bullish on the commercial development of AI, anticipating a substantial increase in the demand for computing power, thereby creating a significant market opportunity for AI chips.

Per data cited by TechNews, it has indicated that the global AI chip market reached USD 580 billion in 2022 and is projected to exceed a trillion dollars by 2030.

Leading AI chip manufacturers like NVIDIA are naturally poised to continue benefiting from this trend. At the same time, Chinese AI chip companies also have the opportunity to narrow the gap and accelerate growth within the vast AI market landscape.

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