On the 15th, Microsoft introducing its first in-house AI chip, “Maia.” This move signifies the entry of the world’s second-largest cloud service provider (CSP) into the domain of self-developed AI chips. Concurrently, Microsoft introduced the cloud computing processor “Cobalt,” set to be deployed alongside Maia in selected Microsoft data centers early next year. Both cutting-edge chips are produced using TSMC’s advanced 5nm process, as reported by UDN News.

Amidst the global AI fervor, the trend of CSPs developing their own AI chips has gained momentum. Key players like Amazon, Google, and Meta have already ventured into this territory. Microsoft, positioned as the second-largest CSP globally, joined the league on the 15th, unveiling its inaugural self-developed AI chip, Maia, at the annual Ignite developer conference.

These AI chips developed by CSPs are not intended for external sale; rather, they are exclusively reserved for in-house use. However, given the commanding presence of the top four CSPs in the global market, a significant business opportunity unfolds. Market analysts anticipate that, with the exception of Google—aligned with Samsung for chip production—other major CSPs will likely turn to TSMC for the production of their AI self-developed chips.

TSMC maintains its consistent policy of not commenting on specific customer products and order details.

TSMC’s recent earnings call disclosed that 5nm process shipments constituted 37% of Q3 shipments this year, making the most substantial contribution. Having first 5nm plant mass production in 2020, TSMC has introduced various technologies such as N4, N4P, N4X, and N5A in recent years, continually reinforcing its 5nm family capabilities.

Maia is tailored for processing extensive language models. According to Microsoft, it initially serves the company’s services such as $30 per month AI assistant, “Copilot,” which offers Azure cloud customers a customizable alternative to Nvidia chips.

Borkar, Corporate VP, Azure Hardware Systems & Infrastructure at Microsoft, revealed that Microsoft has been testing the Maia chip in Bing search engine and Office AI products. Notably, Microsoft has been relying on Nvidia chips for training GPT models in collaboration with OpenAI, and Maia is currently undergoing testing.

Gulia, Executive VP of Microsoft Cloud and AI Group, emphasized that starting next year, Microsoft customers using Bing, Microsoft 365, and Azure OpenAI services will witness the performance capabilities of Maia.

While actively advancing its in-house AI chip development, Microsoft underscores its commitment to offering cloud services to Azure customers utilizing the latest flagship chips from Nvidia and AMD, sustaining existing collaborations.

Regarding the cloud computing processor Cobalt, adopting the Arm architecture with 128 core chip, it boasts capabilities comparable to Intel and AMD. Developed with chip designs from devices like smartphones for enhanced energy efficiency, Cobalt aims to challenge major cloud competitors, including Amazon.
(Image: Microsoft)

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.

According to CTEE, NVIDIA’s forthcoming AI server, the GB200 (B100), slated for a 2024 release, has entered the certification phase in the supply chain. Recent market rumors suggest that Foxconn, originally intended to secure orders for the B100 board, faced certification challenges. As a result, Wistron has maintained its initial order share.

Additionally, it is worth noting that Ingrasys, a subsidiary of Foxconn, is actively manufacturing the H100 product and is a strong contender to secure orders.

Unofficial sources indicate that NVIDIA initially considered making Foxconn the second supplier for AI-GPU server baseboard in the upcoming B100 series. However, due to yield concerns and other factors, Wistron is still expected to receive 100% of the orders. Wistron has also capitalized on the opportunity to secure orders for the front-end AI-GPU module, which appears to be a successful move.

The rapid evolution of AI has intensified competition among assembly plants. Wistron and Foxconn play crucial roles as suppliers for NVIDIA’s current mainstream H100 series GPU modules and baseboards.

Wistron, as the exclusive supplier for H100 baseboards in the NVIDIA DGX and HGX architectures, also holds the exclusive role of providing mainboards and assembling AI servers for DGX. As shipments of the H100 series AI servers, built on the NVIDIA DGX and HGX frameworks, steadily increase in the latter half of the year, Wistron’s AI server-related product business shows consistent growth.

It’s worth noting that Ingrasys is responsible for manufacturing the H100. NVIDIA’s founder, Jensen Huang, and Foxconn’s Chairman, Young Liu, jointly attended a technology event, highlighted the close collaboration in between, underscoring Foxconn’s determination to secure B100 orders.

(Image: Wistron)

SK hynix has introduced LPDDR5T (Low Power Double Data Rate 5 Turbo), a mobile DRAM with a remarkable 9.6Gbps speed. What sets this apart is its compatibility with Qualcomm’s new Snapdragon 8 Gen 3 Mobile Platform.

LPDDR5T features a 16GB-capacity version, delivering data processing speeds of 77GB per second while maintaining low power consumption. Its efficiency and speed are achieved through the incorporation of HKMG (High-K Metal Gate) technology, which reduces power usage and increases processing speed.

“Generative AI applications running on our new Snapdragon 8 Gen 3 enables exciting new use cases by executing LLMs and LVMs on device with minimal latency and at the lowest power,” said Ziad Asghar, Senior Vice President of Product Management at Qualcomm Technologies, Inc. “Our collaboration with SK hynix pairs the fastest mobile memory with our latest Snapdragon mobile platform and delivers amazing on-device, ultra-personalized AI experiences such as AI virtual assistants for smartphone users.”

“We are thrilled that we have met our customers’ needs for the ultra-high performance mobile DRAM with the provision of the LPDDR5T,” said Sungsoo Ryu, Head of DRAM Product Planning at SK hynix.

This collaboration between SK hynix and Qualcomm signals a new era for smartphones, aims to provide on-device, ultra-personalized AI experiences. As smartphones continue to evolve with enhanced DRAM for mobile, the partnership is set to strengthen and drive innovation in this space, positioning the devices as key vehicles for AI applications in the coming years.

(Image: SK hynix)

As Jiwei reported, AMD, although trailing NVIDIA in AI, has recently clinched significant deals, earning the trust of two major clients, Oracle and IBM. Oracle plans to integrate AMD’s Instinct MI300X AI chips into their cloud services, complemented by HPC GPUs. Additionally, as per insights from Ming-Chi Kuo, TF International Securities analyst, IBM is set to leverage AMD’s Xilinx FPGA solutions to handle artificial intelligence workloads.

Oracle’s extensive cloud computing infrastructure faces challenges due to a shortage of NVIDIA GPUs. Nonetheless, Oracle maintains an optimistic outlook. They aim to expand the deployment of the H100 chip by 2024 while considering AMD’s Instinct MI300X as a viable alternative. Oracle has decided to postpone the application of their in-house chips, a project with a multi-year timeline. Instead, they are shifting their focus to AMD’s high-performance AI chip, the MI300X, well-regarded for its impressive capabilities.

Reports indicate that Oracle intends to introduce these processor chips into their infrastructure in early 2024.

Similarly, IBM is exploring chip options beyond NVIDIA. Their new AI inference platform relies on NeuReality’s NR1 chip, manufactured on TSMC’s 7nm process. AMD plays a pivotal role in NeuReality’s AI solution by providing the essential FPGA chips. Foxconn is gearing up for AI server production using this technology in the Q4 2023.

Guo also pointed out that, although Nvidia remains the dominant AI chip manufacturer in 2024, AMD strengthens partnerships with platform service providers/CSPs like Microsoft and Amazon while acquiring companies like Nod.ai. This positions AMD to potentially narrow the AI gap with Nvidia starting in 2025. This collaboration also affirms that AMD remains unaffected by the updated U.S. ban on shipping AI chips to China.

(Image: AMD)