While all eyes are on Intel’s restructuring plan, which features the foundry unit’s spin-off and the delay of Germany and Poland factories, there is another critical decision regarding its photonics business.

According to Intel’s announcement, the tech giant is moving Integrated Photonics Solutions (IPS) into its Data Center and Artificial Intelligence division (DCAI), as it tries to drive a more focused R&D plan that’s fully aligned with its top business priorities.

This shuffle seems to be reasonable, as earlier this year, Intel has achieved a milestone in integrated photonics technology for high-speed data transmission, and the two arenas seem to be inseparable.

A few months ago, Intel demonstrated the industry’s most advanced and first-ever fully integrated optical compute interconnect (OCI) chiplet co-packaged with an Intel CPU and running live data. According to Intel, the OCI chiplet represents a leap forward in high-bandwidth interconnect by enabling co-packaged optical input/output (I/O) in emerging AI infrastructure for data centers and high performance computing (HPC) applications.

A report by Photonics Spectra notes that Intel’s IPS division focuses on technologies such as light generation, amplification, detection, modulation, CMOS interface circuits, and package integration.

Here’s why this technology matters: As chipmakers advance Moore’s Law, increasing transistor density, signal loss during transmission becomes a significant issue because chips use electricity to transmit signals. Silicon photonics technology addresses this problem by using optical signals instead of electrical ones, allowing for high-speed data transmission, greater bandwidth, and faster data processing.

Intel has been developing silicon photonics technology for over 30 years. Since the launch of its silicon photonics platform in 2016, Intel has shipped over 8 million photonic integrated circuits (PICs) and more than 3.2 million integrated on-chip lasers, according to its press release. These products have been adopted by numerous large-scale cloud service providers.

In addition to Intel, rivals such as AMD and TSMC are also accelerating the development of next-generation silicon photonic solution.

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• [News] Venturing into Silicon Photonics, AMD Reportedly Seeks Partnerships in Taiwan
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(Photo credit: Intel)

ByteDance, the parent company of TikTok, is said to be collaborating with TSMC, eyeing for the mass production of two self-developed AI chips by 2026, according to reports by Economic Daily News and The Information.

ByteDance’s AI chips are expected to be made with TSMC’s 5nm node, which would be one generation behind the foundry giant’s most advanced process, the reports suggest, making the move comply with the U.S. export regulations to China. The chips are similar to NVIDIA’s next-generation flagship AI chip, Blackwell, which are manufactured with TSMC’s 4NP node.

Citing sources familiar with the matter, the reports note that the tech giant in China aims to reduce its reliance on NVIDIA for AI model development. Though the chips are still in the design phase and the plan is subject to change, ByteDance’s self-designed chips could save billions of dollars compared to purchasing NVIDIA’s products, according to the reports.

The Information estimates that ByteDance’s spending on developing generative AI models has been increasing, and it is rumored that the company has ordered over 200,000 NVIDIA H20 chips this year, costing it over USD 2 billion, with some orders still pending delivery.

In response to US export bans, NVIDIA launched AI chip H20, L20 and L2, specially designed for the Chinese market earlier this year. According to a previous report by Wccftech, H20 GPU has 41% fewer Cores and 28% lower performance versus H100. Still, the product is reportedly seeing strong demand for AI servers among Chinese Cloud Service Providers (CSPs) and enterprises, including Huawei and Tencent.

However, due to its lower computing power, Chinese companies need to purchase more H20 chips to build clusters with equivalent computing capacity, which raises costs, Economic Daily News notes.

According to TSMC’s financial report in the second quarter, North American clients contributed 65% of its total revenue. While China, the second-largest market, contributed 16% of its quarterly revenue, with a significant jump from 9% in the first quarter and 12% during the same period last year.

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• [News] China Plans to Set at Least 50 AI Standards by 2026, from Language Models to Semiconductors
• [News] Broadcom Reportedly Developing 5nm AI Chip with ByteDance, Manufactured by TSMC

(Photo credit: ByteDance)

According to a report from wccftech, AMD CEO Lisa Su has indicated that she believes the AI Supercycle has just started, and the company has accelerated its product development plans to meet the substantial market demand.

In addition to NVIDIA, AMD is a significant player in the AI market as well. This is not only due to its market impact but also because the company has significantly expanded its AI product portfolio over the past few quarters, attracting attention from major clients like Microsoft and Amazon.

While AMD has not yet replicated NVIDIA’s success in the market, the company remains optimistic about the future, which is why it believes the AI boom has only just begun.

A few months ago, AMD outlined its AI chip development roadmap for the next year. The “Advancing AI” event in this October will showcase the next-generation Instinct MI325X AI chip.

The flagship Instinct MI350 AI chip is scheduled for release in 2025, followed by the Instinct MI400 AI chip in 2026. Despite AMD’s advancements, there remains a generational gap, as competitor NVIDIA is poised to launch its Blackwell architecture AI chips in the coming months.

This move is particularly noteworthy as AMD is focusing on competing with NVIDIA’s CUDA on the software front.

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• [News] AMD Unifies RDNA and CDNA into UDNA Architecture, Aiming to Compete with NVIDIA’s CUDA
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(Photo credit: AMD)

With AI giants in the Western world, such as OpenAI, Google and Meta, stealing the spotlight by their development of generative AI, some big names in China have introduced their AI models over the past 18 months, according to a latest report by CNBC.

Though trying to keep a low profile, five tech conglomerates in China, including Alibaba, Baidu, ByteDance, Huawei and Tencent, have launched their AI models lately, adding a new dimension to the competitive landscape of the ongoing tech rivalry between China and the U.S. Here is a brief introduction of the major Chinese AI models developed by the country’s leading tech firms, based on the information compiled by CNBC and other media reports.

Alibaba: Tongyi Qianwen

In November, 2023, a report by pandaily notes that Alibaba Cloud released its AI model, Tongyi Qianwen 1.0 a few months ago, while the 2.0 version was introduced later in the same year. Another report by South China Morning Post states that as of May, Alibaba reports that its Tongyi Qianwen AI models, often referred to as Qwen, are utilized by more than 90,000 corporate clients across China.

CNBC notes that in terms of Qwen, the company has developed various versions tailored to different functions. For instance, one model specializes in generating content or solving math problems, while another handles audio inputs and provides text-based responses.

It is worth noting that as some Qwen models are open-sourced, developers are allowed to download and use them under certain restrictions, according to CNBC.

Baidu: ERNIE

As one of China’s leading internet companies, Baidu was among the first to introduce generative AI applications in the country. A report by The Verge notes that the Ernie chatbot was available for download in August, 2023, after the approval by the Chinese government.

CNBC reports that Baidu intends to compete with OpenAI’s ChatGPT with Ernie Bot, as the company claims the bot to have 300 million users.

According to CNBC, ahead of the launch of its “Turbo” version, which took place in late June, Baidu stated that its Ernie 4.0 offers capabilities comparable to OpenAI’s GPT-4. According to Baidu, this foundational model has advanced understanding and reasoning abilities.

Similar to other companies, Baidu is offering access to its AI model through its cloud computing services, CNBC says.

ByteDance: Doubao

TikTok parent company ByteDance, though entered the AI race later than competitors like Baidu and Alibaba, has surprised the market with its low-cost Doubao model, which was launched in May, 2024.

According to a report by technode, the model can process 2 million Chinese characters, equivalent to 1.25 million tokens, for just RMB 1 (USD 0.14). In comparison, OpenAI’s latest multimodal model, GPT-4o, costs USD 5 per million input tokens.

CNBC notes that Doubao has various capabilities, including voice generation and coding support for developers.

Huawei: Pangu

Introduced by Huawei in 2021 as the world’s largest pre-trained Chinese large language models (LLMs) with over 100 billion parameters, the Pangu models are now entering their fourth iteration, according to Counterpoint. In May, 2024, the latest Pangu models are said to boast 230 billion parameters.

Interesting enough, Huawei has adopted a different strategy from its competitors with its Pangu AI models, CNBC remarks. The tech giant focuses on developing industry-specific models tailored to sectors like government, finance, manufacturing, mining, and meteorology.

For instance, Huawei claims that its Pangu Meteorology Model can predict a typhoon’s trajectory 10 days in advance in just 10 seconds, a task that previously took four to five hours, according to CNBC.

Tencent: Hunyuan

Last year, Tencent introduced its foundational model, Hunyuan, which is accessible through Tencent’s cloud computing services.

According to CNBC, Tencent has highlighted Hunyuan’s strong Chinese language processing abilities and advanced logical reasoning, supporting features like image generation and text recognition. The model is designed for use across industries such as gaming, social media, and e-commerce.

As the operator of China’s largest messaging app, WeChat, Tencent launched an AI chatbot this year based on the Hunyuan model. The AI assistant, named Yuanbao, can access information and content from WeChat, setting it apart from competitors, CNBC notes.

Notably, China’s large language models, just like its rivals in the West, rely on the strong computing power of AI chips. A previous report by Reuters in November, 2023, states that Tencent is said to have stockpiled a substantial reserve of AI chips from NVIDIA, as the company prepares in advance to train its Hunyuan AI models for the following generations.

How far will the tech giants in China be able to push the boundaries of AI models? The answer may lie in the development of the country’s domestic chips, as the U.S. authority already banned the export to China of AI chips.

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• [News] China’s Core AI Industry Achieved a Market Size of Nearly CNY 600 Billion

(Photo credit: Baidu)

In recent years, data center has been one of the key areas for GaN (Gallium Nitride) manufacturers to tap power electronics market, and GaN applications in the data center power supply market have taken a big step forward. Notably, the rise of AI technology has further fueled this market at present.

In the AI ecosystem, data centers have enormous demands for high-speed computing and power. According to a TrendForce report, NVIDIA’s Blackwell platform will be officially launched in 2025, replacing the existing Hopper platform, and will become NVIDIA’s primary solution for high-end GPU, accounting for nearly 83% of all high-end products.

For high-performance AI servers like the B200 and GB200, a single GPU can consume more than 1,000W of power.

Facing soaring power demands, the power specifications for each data center rack will increase from 30-40kW to 100kW, posing significant challenges for data center power systems. The combination of GaN and liquid cooling technologies will be critical to improving energy efficiency in AI data centers.

The hike in chip power consumption requires servers to achieve higher power density and efficiency.

GaN, which reduces energy losses and increases power density, is now seen as one of the key technologies for optimizing energy efficiency in AI data centers, which has attracted many players, including Infineon, Texas Instruments (TI), Navitas, Innoscience, Transphorm, CorEnergy, Danxi Tech, and GaNext, to join the race.

Among them, both Navitas and Infineon have unveiled their AI data center power roadmaps.

Infineon’s AI Data Center Power Roadmap (Source: Infineon)

Combining the unique advantages of Si (Silicon), SiC (Silicon Carbide), and GaN (Gallium Nitride), Infineon has launched a 3 kW PSU and a 3.3 kW PSU, with an 8 kW PSU expected to be available in the first quarter of 2025.

The new 8 kW PSU will support AI racks with outputs of up to 300 kW or more. Compared to the 32 W/in³ density of the 3 kW PSU, its efficiency and power density will increase to 100 W/in³, further reducing system size and lowering operator costs.

In terms of GaN technology, Infineon’s CoolGaN™ solution can provide over 99% system efficiency in PFC topologies. Moreover, GaN Systems, acquired by Infineon, already released a 3.2kW AI server power supply as early as 2022 and unveiled its fourth-generation GaN platform in 2023.

The new platform achieves efficiency exceeding the Titanium level, with power density increased from 100W/in³ to 120W/in³. Thereby, the industry highly expects the synergistic effect created by the combination of these two companies.

Navitas introduced its GaNSafe™ and Gen-3 Fast SiC technology last year, along with a 4.5kW CRPS design, achieving more than double the power density of traditional silicon solutions. In July this year, Navitas unveiled its CRPS185 4.5kW AI data center server power solution, with a power density of 137W/in³ and over 97% efficiency.

Navitas AI Data Center Power Roadmap (Source: Navitas)

Navitas revealed that over 60 customer projects involving 3.2kW and 4.5kW power solutions for data centers are currently under development.

These projects are expected to bring millions of dollars in revenue growth for Navitas’ GaN and SiC business between 2024 and 2025. It aims to begin small-scale production of AI data center power solutions in 2024.

Aside from Infineon and Navitas, other manufacturers like TI, EPC, CorEnergy, GaNext, and Innoscience also set sights on this market.

TI reached an agreement with Delta, the world’s largest server power supply provider (with nearly 50% market share), as early as 2021. Based on GaN technology and TI’s C2000™ MCU real-time control solution, they are developing high-efficiency, high-power server PSU for data centers.

Thus, their joint efforts and future fruit in the AI server power market are highly anticipated.

Beyond AI data center server, humanoid robot industry that enjoys burgeoning growth this year, also injects new vitality into the GaN market.

Humanoid robot is assembled by sensing, control, motor, and battery systems, in which GaN can has its place in LiDAR system, motor drive, DC-DC converter, and battery BMS, among which motor drive plays a critical role.

According to TrendForce, the demand for motor driver in humanoid robot has skyrocketed due to the mounting demands for degrees of freedom.

To achieve higher power output, high-power-density, high-efficiency, and fast-response motor driver are in demand, and GaN is a perfect fit for it, which also has the ability to strengthen overall robot performance in terms of heat management, compact design, and overall system design.

It is reported that Siemens, Yaskawa Electric, and Elmo have already integrated GaN technology into their robotic motors, and the GaN industry chain is gearing up for seizing more opportunities.

Currently, companies such as TI, EPC, Transphorm, Innoscience, Navitas, and CorEnergy are actively promoting GaN adoption in motor drive market. Among them, Transphorm has supplied GaN FET products for Yaskawa Electric’s new servo motor.

TrendForce points out that future robots will exceed our imagination, with precise, fast, and powerful movement capabilities as the key parts, which will inevitably push the motors required to drive these movements advance forward, and this is regarded as a boon for GaN technology.

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• [News] Global GaN Power Device Market Size Expected to Reach USD 4.376 Billion in 2030, CAGR of 49%
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(Photo credit: Infineon)