NVIDIA’s latest financial report for FY2Q24 reveals that its data center business reached US$10.32 billion—a QoQ growth of 141% and YoY increase of 171%. The company remains optimistic about its future growth. TrendForce believes that the primary driver behind NVIDIA’s robust revenue growth stems from its data center’s AI server-related solutions. Key products include AI-accelerated GPUs and AI server HGX reference architecture, which serve as the foundational AI infrastructure for large data centers.

TrendForce further anticipates that NVIDIA will integrate its software and hardware resources. Utilizing a refined approach, NVIDIA will align its high-end, mid-tier, and entry-level GPU AI accelerator chips with various ODMs and OEMs, establishing a collaborative system certification model. Beyond accelerating the deployment of CSP cloud AI server infrastructures, NVIDIA is also partnering with entities like VMware on solutions including the Private AI Foundation. This strategy extends NVIDIA’s reach into the edge enterprise AI server market, underpinning steady growth in its data center business for the next two years.

NVIDIA’s data center business surpasses 76% market share due to strong demand for cloud AI

In recent years, NVIDIA has been actively expanding its data center business. In FY4Q22, data center revenue accounted for approximately 42.7%, trailing its gaming segment by about 2 percentage points. However, by FY1Q23, data center business surpassed gaming—accounting for over 45% of revenue. Starting in 2023, with major CSPs heavily investing in ChatBOTS and various AI services for public cloud infrastructures, NVIDIA reaped significant benefits. By FY2Q24, data center revenue share skyrocketed to over 76%.

NVIDIA targets both Cloud and Edge Data Center AI markets

TrendForce observes and forecasts a shift in NVIDIA’s approach to high-end GPU products in 2H23. While the company has primarily focused on top-tier AI servers equipped with the A100 and H100, given positive market demand, NVIDIA is likely to prioritize the higher-priced H100 to effectively boost its data-center-related revenue growth.

NVIDIA is currently emphasizing the L40s as their flagship product for mid-tier GPUs, meaning several strategic implications: Firstly, the high-end H100 series is constrained by the limited production capacity of current CoWoS and HBM technologies. In contrast, the L40s primarily utilizes GDDR memory. Without the need for CoWos packaging, it can be rapidly introduced to the mid-tier AI server market, filling the gap left by the A100 PCle interface in meeting the needs of enterprise customers.

Secondly, the L40s also target enterprise customers who don’t require large parameter models like ChatGPT. Instead, it focuses on more compact AI training applications in various specialized fields, with parameter counts ranging from tens of billions to under a hundred billion. They can also address edge AI inference or image analysis tasks. Additionally, in light of potential geopolitical issues that might disrupt the supply of the high-end GPU H series for Chinese customers, the L40s can serve as an alternative. As for lower-tier GPUs, NVIDIA highlights the L4 or T4 series, which are designed for real-time AI inference or image analysis in edge AI servers. These GPUs underscore affordability while maintaining a high-cost-performance ratio.

HGX and MGX AI server reference architectures are set to be NVIDIA’s main weapons for AI solutions in 2H23

TrendForce notes that recently, NVIDIA has not only refined its product positioning for its core AI chip GPU but has also actively promoted its HGX and MGX solutions. Although this approach isn’t new in the server industry, NVIDIA has the opportunity to solidify its leading position with this strategy. The key is NVIDIA’s absolute leadership stemming from its extensive integration of its GPU and CUDA platform—establishing a comprehensive AI ecosystem. As a result, NVIDIA has considerable negotiating power with existing server supply chains. Consequently, ODMs like Inventec, Quanta, FII, Wistron, and Wiwynn, as well as brands such as Dell, Supermicro, and Gigabyte, are encouraged to follow NVIDIA’s HGX or MGX reference designs. However, they must undergo NVIDIA’s hardware and software certification process for these AI server reference architectures. Leveraging this, NVIDIA can bundle and offer integrated solutions like its Arm CPU Grace, NPU, and AI Cloud Foundation.

It’s worth noting that for ODMs or OEMs, given that NVIDIA is expected to make significant achievements in the AI server market for CSPs from 2023 to 2024, there will likely be a boost in overall shipment volume and revenue growth of AI servers. However, with NVIDIA’s strategic introduction of standardized AI server architectures like HGX or MGX, the core product architecture for AI servers among ODMs and others will become more homogenized. This will intensify the competition among them as they vie for orders from CSPs. Furthermore, it’s been observed that large CSPs such as Google and AWS are leaning toward adopting in-house ASIC AI accelerator chips in the future, meaning there’s a potential threat to a portion of NVIDIA’s GPU market. This is likely one of the reasons NVIDIA continues to roll out GPUs with varied positioning and comprehensive solutions. They aim to further expand their AI business aggressively to Tier-2 data centers (like CoreWeave) and edge enterprise clients.

According to a report by Taiwan’s Economic Daily, the latest GH200 module released by NVIDIA has seen its assembly orders exclusively undertaken by Foxconn, while the assembly orders for L40S are also entirely managed by Foxconn.

Foxconn has traditionally refrained from commenting on individual business and order dynamics. It is believed that AI chip modules constitute the highest-margin product within the entire server supply chain.

Foxconn has been a longstanding partner of NVIDIA, providing an end-to-end solution across chip modules, baseboards, motherboards, servers, and chassis. Foxconn’s capabilities have facilitated the creation of a comprehensive solution for NVIDIA’s AI server supply chain.

Previously, Foxconn had an exclusive assembly partnership with NVIDIA for the “H100” and “H800” modules, not only retaining the existing orders but also securing a substantial portion of the HGX module orders. Now, reports indicate that Foxconn will exclusively supply even NVIDIA’s newly unveiled GH 200, and the L40S.

Industry sources indicate that due to severe constraints on TSMC’s advanced CoWoS packaging capacity, the scaling up of NVIDIA’s AI chip production has been hindered. However, with new CoWoS production capacity set to gradually open up in the late third quarter to the fourth quarter, shipments of Foxconn’s AI chip modules are anticipated to rapidly increase.

Industry sources reveal that in business negotiations, NVIDIA is known for demanding from its suppliers, but it is also generous in its offerings. As long as suppliers provide products that meet or even exceed expectations, NVIDIA is willing to offer reasonable prices, fostering mutually beneficial relationships with its partners.

(Photo credit: NVIDIA)

US-based CSPs have been establishing SMT production lines in Southeast Asia since late 2022 to mitigate geopolitical risks and supply chain disruptions. TrendForce reports that Taiwan-based server ODMs, including Quanta, Foxconn, Wistron (including Wiwynn), and Inventec, have set up production bases in countries like Thailand, Vietnam, and Malaysia. It’s projected that by 2023, the production capacity from these regions will account for 23%, and by 2026, it will approach 50%.

TrendForce reveals that Quanta, due to its geographical ties, has established several production lines in its Thai facilities centered around Google and Celestica, aiming for optimal positioning to foster customer loyalty. Meanwhile, Foxconn has renovated its existing facilities in Hanoi, Vietnam, and uses its Wisconsin plant to accommodate customer needs. Both Wistron and Wiwynn are progressively establishing assembly plants and SMT production lines in Malaysia. Inventec’s current strategy mirrors that of Quanta, with plans to build SMT production lines in Thailand by 2024 and commence server production in late 2024.

CSPs aim to control the core supply chain, AI server supply chain trends toward decentralization

TrendForce suggests that changes in the supply chain aren’t just about circumventing geopolitical risks—equally vital is increased control over key high-cost components, including CPUs, GPUs, and other critical materials. With rising demand for next-generation AI and Large Language Models, supply chain stockpiling grows each quarter. Accompanied by a surge in demand in 1H23, CSPs will become especially cautious in their supply chain management.

Google, with its in-house developed TPU machines, possesses both the core R&D and supply chain leadership. Moreover, its production stronghold primarily revolves around its own manufacturing sites in Thailand. However, Google still relies on cooperative ODMs for human resource allocation and production scheduling, while managing other materials internally. To avoid disruptions in the supply chain, companies like Microsoft, Meta, and AWS are not only aiming for flexibility in supply chain management but are also integrating system integrators into ODM production. This approach allows for more dispersed and meticulous coordination and execution of projects.

Initially, Meta heavily relied on direct purchases of complete server systems, with Intel’s Habana system being one of the first to be integrated into Meta’s infrastructure. This made sense since the CPU for their web-type servers were often semi-custom versions from Intel. Based on system optimization levels, Meta found Habana to be the most direct and seamless solution. Notably, it was only last year that Meta began to delegate parts of its Metaverse project to ODMs. This year, as part of its push into generative AI, Meta has also started adopting NVIDIA’s solutions extensively.

Dell, a major server brand, placed a substantial order for AI servers just before NVIDIA’s Q2 financial report. This move is reshaping Taiwan’s supply chain dynamics, favoring companies like Wistron and Lite-On.

Dell is aggressively entering the AI server market, ordering NVIDIA’s top-tier H100 chips and components. The order’s value this year is estimated in hundreds of billions of Taiwanese dollars, projected to double in the next year. Wistron and Lite-On are poised to benefit, securing vital assembly and power supply orders. EMC and Chenbro are also joining the supply chain.

Dell’s AI server order, which includes assembly (including complete machines, motherboards, GPU boards, etc.) and power supply components, stands out with its staggering value. The competition was most intense in the assembly sector, ultimately won by Wistron. In the power supply domain, industry leaders like Delta, Lite-On, secured a notable share, with Lite-On emerging as a winner, sparking significant industry discussions.

According to Dell’s supply chain data, AI server inventory will reach 20,000 units this year and increase next year. The inventory primarily features the highest-end H100 chips from NVIDIA, with a few units integrating the A100 chips. With each H100 unit priced at $300,000 and A100 units exceeding $100,000, even with a seemingly modest 20,000 units, the total value remains in the billions of New Taiwan Dollars.

Wistron is a standout winner in Dell’s AI server assembly order, including complete machines, motherboards, and GPU boards. Wistron has existing H100 server orders and will supply new B100 baseboard orders. Their AI server baseboard plant in Hsinchu, Taiwan will expand by Q3 this year. Wistron anticipates year-round growth in the AI server business.

In just a short span of six months, AI has evolved from generating text, images, music, and code to automating tasks and producing agents, showcasing astonishing capabilities. TrendForce has issued a new report titled “Surge in AIGC Applications to Drive Long-Term Demand for AI Servers.” Beyond highlighting the latest developments in AI, the report also delves into strategies adopted by governments and industries to ensure the positive trajectory of AI’s development. It analyzes the projected timeline for the widespread implementation of AIGC applications and their impact on the demand for AI servers.

• AIGC Application Wave Expected to Arrive by 2025 Once Rules are Set

While the AIGC application frenzy in the first half of 2023 has raised concerns, it has also prompted governments and industries to actively address potential risks and issues stemming from AIGC applications, along with devising corresponding solutions. Currently, both the government and industries have strategies in place to regulate AIGC applications in terms of legal oversight, privacy protection, identity establishment, reliability enhancement, security augmentation, and copyright maintenance.

Considering the time required for governments to draft legislation and industries to enhance AI’s reliability, security, and copyright protection, it is estimated that the rules of the AIGC application will gradually solidify by late 2024 to early 2025, paving the way for the AIGC application surge around 2025.

Beyond the five major categories of AIGC applications—text generation, image generation, music generation, video generation, and code generation—AIGC technology-based applications like AI customer service, personalized AI assistants, AI search, and AI productivity tools are also gaining prominence. In the realm of gaming, whether in VR or open-world games, AIGC technology is set to significantly enhance immersion and freedom, ushering in revolutionary experiences.

• Long-Term Demand for AI Servers to Benefit Server Brands and ODMs

To secure a dominant position in the AI technology industry and embrace the upcoming AIGC application wave, application service providers, tech giants, national institutions, and startups are competing to bolster their AI computing resources. As core computing components experience increased shipments, the shipment volume of AI servers, which serve as foundational computing units, is also expected to surge.

In the proactive year of 2023, where institutions and enterprises are aggressively building computing resources, the AI server shipment volume is projected to grow substantially. Given the limited upstream semiconductor capacity, this momentum is likely to extend into 2024.

By 2025, propelled by the AIGC application frenzy, AI server shipments are poised for further stimulation. Consequently, due to institutions and businesses preemptively establishing computing resources and the projected timeline for large-scale AIGC application implementation, the AI server market is anticipated to witness a sustained demand surge. Given the intricate manufacturing of AI servers and their higher degree of customization, their profitability exceeds that of general servers. With the continual growth in AI server shipments, relevant server brands and ODM manufacturers are poised to reap significant benefits.