[News] Hurdles in Acquiring NVIDIA’s High-End Products: Assessing the Progress of Eight Chinese AI Chip Companies in Self-Development

2024-02-22 Semiconductors editor

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.


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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(Photo credit: iStock)

Please note that this article cites information from TechNews and Reuters.