In an interview with Chinese media Sina, Lisa Su, CEO of AMD, emphasized that AI is the most revolutionary technology in the past 50 years. She believes that AI-powered PCs will play a crucial role in driving the growth of the PC market this year.

Su led AMD’s AI PC Innovation Summit in Beijing last week, showcasing the development momentum within China’s AI PC ecosystem. She shared these insights during interviews with Sina, which was then published on March 26th.

Lisa Su asserts that AI is propelling a revolution, marking the most transformative technology in nearly 50 years, swiftly reshaping all facets of the tech industry. From data centers to AI-powered PCs and edge computing, AMD is excited about the opportunities presented by this new era of computing.

Su emphasizes that PCs serve as the daily tools for users to interact with AI through personalized experiences. Leveraging Ryzen AI’s leading edge and extensive ecosystem partnerships, AMD aims to deliver seamless AI experiences from the cloud to the PC.

Lisa Su acknowledges that the global PC market saw a decline post-pandemic, but anticipates some level of growth this year, driven by AI-powered PCs prompting consumers to upgrade their devices.

She believes that while most AI PCs currently target the high-end segment, over time, they are expected to penetrate every price range.

Regarding the applications of AI PCs, Su finds communication, productivity, and creativity particularly exciting. Many applications are still in their early stages, but she expects to see more developments in the coming years.

Lisa Su also mentioned a compelling incentive for people to upgrade to AI PCs: increased efficiency. She posed a question to the media, asking if users would be willing to purchase an AI PC if it could save them 5 hours of work per week. In her view, “everyone’s answer would be YES.”

AMD is strategically positioning itself in the AI market. In December last year, it announced that its accelerated processing unit (APU) MI300A had entered mass production, while the AI accelerator GPU MI300X had begun shipping. Meanwhile, its new Ryzen 8040 series laptop processors have also hit the market, aiming to capture the AI PC market.

To deliver AI experience on PCs, AMD utilizes three computing engines: CPU based on Zen architecture, GPU based on RDNA architecture, and the XDNA-based AI engine, also known as the Neural Processing Unit (NPU). Additionally, its Ryzen 8040 series processors offer leading-edge computing and AI experiences. By the end of this year, the company plans to engage over 150 independent software vendors in developing for Ryzen AI.

TrendForce previously issued an analysis in a press release, indicating that the AI PC market is propelled by two key drivers: Firstly, demand for terminal applications, mainly dominated by Microsoft through its Windows OS and Office suite, is a significant factor. Microsoft is poised to integrate Copilot into the next generation of Windows, making Copilot a fundamental requirement for AI PCs.

Secondly, Intel, as a leading CPU manufacturer, is advocating for AI PCs that combine CPU, GPU, and NPU architectures to enable a variety of terminal AI applications.

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• [News] AMD Hosts Innovation Summit, CEO Lisa Su Highlights the Beginning of AIPC Era
• [News] NVIDIA and AMD Clash in AI Chip Market, as TSMC Dominates Orders with Strong Momentum in Advanced Processes

(Photo credit: AMD)

As per a report from TechNews, Apple’s pivot into AI, abandoning its “Project Titan” for electric cars, signals a shift towards Generative AI. The report further cites sources indicate that Foxconn may provide AI servers to Apple and is currently in testing phase.

Regarding this matter, Foxconn responded with no comment on individual clients or products.

According to reports from Economic Daily News, Apple has conducted extensive AI feature testing and, given Foxconn’s global leadership in server manufacturing, it has emerged as Apple’s preferred partner for the AI project.

In addition to Apple, during a recent financial conference, Dell’s COO, Jeff Clarke, disclosed that NVIDIA is set to launch a new generation server GPU, “B200,” based on the Blackwell architecture in 2025. Notably, this revelation wasn’t part of NVIDIA’s product roadmap released in October 2023, and the company has not officially mentioned this product. 

Currently, the H100 utilizes TSMC’s 4-nanometer process technology, with Foxconn securing approximately 90% of the assembly orders last year. While the fabrication process for the B100 and B200 chips remains unconfirmed, industry expectations cited by the report have pointed to the 3-nanometer process.

Previously, media speculation cited by the report from Commercial Times stated that although the B100 chip boasts computational power at least twice that of the H200 and four times that of the H100, still, B100’s tenure in the market is anticipated to be short-lived, with the B200 emerging as the mainstream product. It is rumored that Foxconn Industrial Internet will handle the manufacturing for the B200.

Foxconn’s Chairman Young Liu previously indicated a strong demand for AI servers, with Foxconn securing new projects continuously.

Foxconn spokesperson James Wu noted that Foxconn Group commands over 40% market share in the server industry, particularly in the mid-to-high-end products related to AI servers. Foxconn closely collaborates with customers and aims to maintain its dominance, anticipating substantial contributions once the entire supply chain stabilizes.

Read more

• [News] Dell Leak Reveals NVIDIA’s Potential B200 Launch Next Year
• [News] SK Hynix Announces Complete Sales of HBM for the Year, Memory Market Poised for Recovery

(Photo credit: Foxconn)

Microsoft is reportedly developing a customized network card for AI servers, as per sources cited by global media The Information. This card is expected to enhance the performance of its in-house AI chip Azure Maia 100 while reducing dependency on NVIDIA as the primary supplier of high-performance network cards.

Leading this product initiative at Microsoft is Pradeep Sindhu, co-founder of Juniper Networks. Microsoft acquired Sindhu’s data center technology startup, Fungible, last year. Sindhu has since joined Microsoft and is leading the team in developing this network card.

According to the Information, this network card is similar to NVIDIA’s ConnectX-7 interface card, which supports a maximum bandwidth of 400 Gb Ethernet and is sold alongside NVIDIA GPUs.

Developing high-speed networking equipment tailored specifically for AI workloads may take over a year. If successful, it could reduce the time required for OpenAI to train models on Microsoft AI servers and lower the costs associated with the training process.

In November last year, Microsoft unveiled the Azure Maia 100 for data centers, manufactured using TSMC’s 5-nanometer process. The Azure Maia 100, introduced at the conference, is an AI accelerator chip designed for tasks such as running OpenAI models, ChatGPT, Bing, GitHub Copilot, and other AI workloads.

Microsoft is also in the process of designing the next generation of the chip. Not only is Microsoft striving to reduce its reliance on NVIDIA, but other companies including OpenAI, Tesla, Google, Amazon, and Meta are also investing in developing their own AI accelerator chips. These companies are expected to compete with NVIDIA’s flagship H100 AI accelerator chips.

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• [Insights] Microsoft Unveils In-House AI Chip, Poised for Competitive Edge with a Powerful Ecosystem
• [Insights] AI Market: A Battleground for Tech Giants as Six Major Companies Develop AI Chips

(Photo credit: Microsoft)

Last year’s AI boom propelled NVIDIA into the spotlight, yet the company finds itself at a challenging crossroads.

According to a report from TechNews, on one hand, NVIDIA dominates in high-performance computing and artificial intelligence, continuously expanding with its latest GPU products. On the other hand, global supply chain instability, rapid emergence of competitors, and uncertainties in technological innovation are exerting unprecedented pressure on NVIDIA.

NVIDIA’s stock price surged by 246% last year, driving its market value past USD 1 trillion and making it the first chip company to achieve this milestone. According to the Bloomberg Billionaires Index, NVIDIA CEO Jensen Huang’s personal wealth has soared to USD 55.7 billion.

However, despite the seemingly radiant outlook for the NVIDIA, as per a report from TechNews, it still faces uncontrollable internal and external challenges.

• Internal Concern 1: CoWoS, HBM Capacity Bottlenecks

The most apparent issue lies in capacity constraints.

Currently, NVIDIA’s A100 and H100 GPUs are manufactured using TSMC’s CoWoS packaging technology. However, with the surge in demand for generative AI, TSMC’s CoWoS capacity is severely strained. Consequently, NVIDIA has certified other CoWoS packaging suppliers such as UMC, ASE, and American OSAT manufacturer Amkor as backup options.

Meanwhile, TSMC has relocated its InFo production capacity from Longtan to Southern Taiwan Science Park. The vacated Longtan fab is being repurposed to expand CoWoS capacity, while the Zhunan and Taichung fabs are also contributing to the expansion of CoWoS production to alleviate capacity constraints.

However, during the earnings call, TSMC also stated that despite a doubling of capacity in 2024, it still may not be sufficient to meet all customer demands.

In addition to TSMC’s CoWoS capacity, industry rumors suggest that NVIDIA has made significant upfront payments to Micron, SK Hynix, to secure HBM3 memory, ensuring a stable supply of HBM memory. However, the entire HBM capacity of Samsung, SK Hynix, and Micron for this year has already been allocated. Therefore, whether the capacity can meet market demand will be a significant challenge for NVIDIA.

• Internal Concern 2: Major Customers Shifting Towards In-house Chips

While cloud service providers (CSPs) fiercely compete for GPUs, major players like Amazon, Microsoft, Google, and Meta are actively investing in in-house AI chips.

Amazon and Google have respectively introduced Trainium and TPU chips, Microsoft announced its first in-house AI chip Maia 100 along with in-house cloud computing CPU Cobalt 100, while Meta plans to unveil its first-generation in-house AI chip MTIA by 2025.

Although these hyperscale customers still rely on NVIDIA’s chips, in the long run, it may impact NVIDIA’s market share, inadvertently positioning them as competitors and affecting profits. Consequently, NVIDIA finds it challenging to depend solely on these hyperscale customers.

• External Challenge 1: Export Control Pressures Lead to Loss of Chinese Customers

Due to escalating tensions between the US and China, the US issued new regulations prohibiting NVIDIA from exporting advanced AI chips to China. Consequently, NVIDIA introduced specially tailored versions such as A800 and H800 for the Chinese market.

However, they were ultimately blocked by the US, and products including A100, A800, H100, H800, and L40S were included in the export control list.Subsequently, NVIDIA decided to introduce new AI GPUs, namely HGXH20, L20 PCIe, and L2 PCIe, in compliance with export policies.

However, with only 20% of the computing power of H100, they are planned for mass production in the second quarter. Due to the reduced performance, major Chinese companies like Alibaba, Tencent, and Baidu reportedly refused to purchase, explicitly stating significant order cuts for the year. Consequently, NVIDIA’s revenue prospects in China appear grim, with some orders even being snatched by Huawei.

Currently, NVIDIA’s sales revenue from Singapore and China accounts for 15% of its total revenue. Moreover, the company holds over 90% market share in the AI chip market in China. Therefore, the cost of abandoning the Chinese market would be substantial. NVIDIA is adamant about not easily giving up on China; however, the challenge lies in how to comply with US government policies and pressures while meeting the demands of Chinese customers.

As per NVIDIA CEO Jensen Huang during its last earnings call, he mentioned that US export control measures would have an impact. Contributions from China and other regions accounted for 20-25% of data center revenue in the last quarter, with a significant anticipated decline this quarter.

He also expressed concerns that besides losing the Chinese market, the situation would accelerate China’s efforts to manufacture its own chips and introduce proprietary GPU products, providing Chinese companies with opportunities to rise.

• External Challenge 2: Arch-Rivals Intel and AMD Begin Their Offensive

In the race to capture the AI market opportunity, arch-rivals Intel and AMD are closely after NVIDIA. As NVIDIA pioneered the adoption of TSMC’s 4-nanometer H100, AMD quickly followed suit by launching the first batch of “Instinct MI300X” for AI and HPC applications last year.

Currently, shipments of MI300X have commenced this year, with Microsoft’s data center division emerging as the largest buyer. Meta has also procured a substantial amount of Instinct MI300 series products, while LaminiAI stands as the first publicly known company to utilize MI300X.

According to official performance tests by AMD, the MI300X outperforms the existing NVIDIA H100 80GB available on the market, posing a potential threat to the upcoming H200 141GB.

Additionally, compared to the H100 chip, the MI300X offers a more competitive price for products of the same level. If NVIDIA’s production capacity continues to be restricted, some customers may switch to AMD.

Meanwhile, Intel unveiled the “Gaudi3” chip for generative AI software last year. Although there is limited information available, it is rumored that the memory capacity may increase by 50% compared to Gaudi 2’s 96GB, possibly upgrading to HBM3e memory. CEO Pat Gelsinger directly stated that “Gaudi 3 performance will surpass that of the H100.”

• External Challenge 3: Startup Underdogs Form AI Platform Alliance in Attempt to Conquer

Several global chip design companies have recently announced the formation of the “AI Platform Alliance,” aiming to promote an open AI ecosystem. The founding members of the AI Platform Alliance include Ampere, Cerebras Systems, Furiosa, Graphcore, Kalray, Kinara, Luminous, Neuchips, Rebellions, and Sapeon, among others.

Notably absent is industry giant NVIDIA, leading to speculation that startups aspire to unite and challenge NVIDIA’s dominance.

However, with NVIDIA holding a 75-90% market share in AI, it remains in a dominant position. Whether the AI Platform Alliance can disrupt NVIDIA’s leading position is still subject to observation.

Read more

• [News] NVIDIA’s Exclusive Chips for China Now Reported to be Available for Pre-Order, Priced Similar to Huawei Products
• [News] NVIDIA CEO Jensen Huang Meets with TSMC Executives in Taiwan, Addressing the Significant AI Chip Shortage

(Photo credit: NVIDIA)

During the “SEMICON Korea 2024” event held recently in Seoul, Chun-hwan Kim, Vice President of global memory giant SK hynix, revealed that the company’s HBM3e has entered mass production, with plans to commence large-scale production of HBM4 in 2026.

According to a report from Business Korea, Chun-hwan Kim stated that SK hynix’s HBM3e memory is currently in mass production, with plans to initiate mass production of HBM4 in 2026.

He noted that with the advent of the AI computing era, generative AI is rapidly advancing, and the market is expected to grow at a rate of 35% annually. The rapid growth of the generative AI market requires a significant number of higher-performance AI chips to support it, further driving the demand for higher-bandwidth memory.

He further commented that the semiconductor industry would face intense survival competition this year to meet the increasing demand and customer needs for memory.

Kim also projected that the HBM market would grow by 40% by 2025, with SK hynix already strategically positioning itself in the market and planning to commence production of HBM4 in 2026.

Meanwhile, previous reports have also indicated that SK hynix expected to establish an advanced packaging facility in the state of Indiana, USA, to meet the demands of American companies, including NVIDIA.

Driven by the wave of AI advancement and demand from China, the Ministry of Trade, Industry and Energy of South Korea recently announced that South Korea’s semiconductor product exports experienced a rebound in 2024. In January, exports reached approximately USD 9.4 billion, marking a year-on-year increase of 56.2% and the largest growth in 73 months.

TrendForce has previously reported the progress of HBM3e, as outlined in the timeline below, which shows that SK hynix already provided its 8hi (24GB) samples to NVIDIA in mid-August.

Read more

• [News] Reports Suggest SK Hynix to Establish Advanced Packaging Facility in the US
• [News] Intel, SK Hynix, and NTT Japan Collaborate to Develop Next-Generation Silicon Photonics Technology

(Photo credit: SK hynix)