{"id":728,"date":"2025-10-23T00:26:36","date_gmt":"2025-10-22T16:26:36","guid":{"rendered":"https:\/\/wiseinfo-tech.com\/?p=728"},"modified":"2025-10-23T00:47:56","modified_gmt":"2025-10-22T16:47:56","slug":"ai%e9%81%8b%e7%ae%97%e5%a4%a7%e8%ba%8d%e5%8d%87%ef%bc%9a%e5%be%9escale-up%e6%88%b0%e5%b1%80%e5%88%b0%e5%b0%88%e5%ae%b6%e6%b4%9e%e5%af%9f%ef%bc%8c%e4%b8%ad%e5%9c%8b%e8%88%87%e5%85%a8%e7%90%83%e7%94%a2","status":"publish","type":"post","link":"https:\/\/wiseinfo-tech.com\/en\/ai%e9%81%8b%e7%ae%97%e5%a4%a7%e8%ba%8d%e5%8d%87%ef%bc%9a%e5%be%9escale-up%e6%88%b0%e5%b1%80%e5%88%b0%e5%b0%88%e5%ae%b6%e6%b4%9e%e5%af%9f%ef%bc%8c%e4%b8%ad%e5%9c%8b%e8%88%87%e5%85%a8%e7%90%83%e7%94%a2\/","title":{"rendered":"The Great Leap Forward in AI Computing: From Scale-Up Battles to Expert Insights, Rebalancing China's Industrial Chain with the Global Industry"},"content":{"rendered":"
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AI Computing Leap: From the Scale-Up Battle to Expert Insights\u2014The Rebalancing of China and the Global Supply Chain
Theme: IC Product Design, Broadband Network Communication<\/h4>\n\n\n\n

The Surge in AI Computing Demand and the Emergence of a New Market Landscape<\/p>\n\n\n\n

Strategic Implications of the Scale-Up Technological Transformation<\/p>\n\n\n\n

Global Competitive Landscape and the Reshuffling of the Industrial Chain<\/p>\n\n\n\n

China\u2019s Unique Development Path in the AI Market<\/p>\n\n\n\n

Re-evaluating the Demand for Switch ICs and Fundamental Components<\/p>\n\n\n\n

Expert Insights: Strategic Choices for the Next Five Years<\/p>\n\n\n\n

I. The Surge in AI Computing Demand and the Emergence of a New Market Landscape<\/h4>\n\n\n\n

In recent years, the surge in AI computing demand has no longer been confined to academic laboratories or tech giants\u2014it has become a pressing reality felt across the entire industrial chain. With the rapid development of generative AI and large language models (LLMs), model parameter counts have leapt from the billions to the trillions, making each training cycle consume an unprecedented amount of computational power.<\/p>\n\n\n\n

This scaling effect is directly reflected in the computing power demand curve: according to published FLOPs growth data, the computational requirements of AI models have increased severalfold annually over the past five years. More importantly, this growth shows no sign of slowing. The continuous extension of context length, the integration of multimodal tasks, and the emergence of agent-based applications are all driving ever-higher demands for memory bandwidth and interconnect latency. As a result, the market is rapidly entering a state of computing power arms race, where any vendor unable to keep pace may be eliminated in a very short time.<\/p>\n\n\n\n

Structurally, earlier stages of AI development focused mainly on the training side, emphasizing large-scale data processing and massive computational power. However, as commercial applications begin to take hold, inference-side demand is rapidly catching up\u2014and even surpassing\u2014training. Once enterprises and consumers embed AI capabilities across applications, inference workloads must handle enormous real-time requests with millisecond-level latency. This means servers must not only support large-scale training runs but also sustain long-term, high-concurrency output. Unlike training workloads, inference emphasizes performance per watt, batch processing efficiency, and scalability.<\/p>\n\n\n\n

This structural shift has reshaped capital expenditures in data centers. Cloud service providers (CSPs) are now compelled to build architectures that balance both ultra-high-performance training clusters and cost-efficient inference infrastructures\u2014a dual strategy known as the coexistence of \u201ctraining islands\u201d and \u201cinference networks.\u201d<\/p>\n\n\n\n

The explosion of AI computing demand has also fundamentally redirected CSPs\u2019 CapEx priorities. Traditionally, CSP investment focused on general-purpose servers, storage, and networking\u2014valuing elasticity and cost-effectiveness. In the AI era, however, CapEx is now concentrated in four key domains: accelerators, high-bandwidth memory, interconnects, and power\/cooling systems. Between 2021 and 2025, financial disclosures from major CSPs show that AI-related expenditures have risen to over 25% of total capital investment, with some exceeding 30%.<\/p>\n\n\n\n

This is not merely an increase in spending\u2014it represents a structural shift. Accelerators such as GPUs and TPUs have become the core hardware, while optical modules, switch ICs, and cooling systems have evolved from supporting components into crucial elements of the value chain. For the supply chain, this means that those who can master high-margin, high-barrier components will command a greater share of the AI industry\u2019s value distribution.<\/p>\n\n\n\n

AI computing demand is also redesigning the logic of data center infrastructure. In the past, data centers centered on general-purpose computing and storage, optimizing for multi-tenancy and cost efficiency. In the AI cycle, however, they must simultaneously address three challenges: high-density power delivery, low-latency interconnects, and high-efficiency cooling. A typical AI server rack now requires 30\u201380 kW of power\u2014and in some cases, more than 100 kW\u2014far beyond conventional standards.<\/p>\n\n\n\n

This compels operators to adopt liquid cooling and immersion cooling technologies at scale to reduce PUE (Power Usage Effectiveness) and enhance system stability. Network topologies are also evolving rapidly\u2014from Clos\/Torus to Dragonfly and programmable Ethernet architectures\u2014to meet AI workloads\u2019 demands for ultra-low-latency and low-diameter connectivity. Such a redefinition of data centers is not merely a hardware upgrade\u2014it represents a reorganization of the entire ecosystem, driving value-chain restructuring across upstream and downstream players.<\/p>\n\n\n\n

As AI computing demand continues to expand, its spillover effects are being felt throughout multiple tiers of the industry.<\/p>\n\n\n\n

Upstream: semiconductor and packaging materials such as HBM memory, ABF substrates, and 2.5D\/3D advanced packaging have become critical bottlenecks.<\/p>\n\n\n\n

Midstream: components like switch ICs, optical transceivers, and cooling systems\u2014once considered peripheral\u2014now define overall system performance ceilings.<\/p>\n\n\n\n

Downstream: cloud services and enterprise applications compete on who can commercialize AI faster and reduce TCO (Total Cost of Ownership) most effectively.<\/p>\n\n\n\n

On a broader scale, the explosion of AI computing demand has also drawn policy-level attention. Many governments now regard computing power as a strategic resource, using subsidies, export controls, and domestic supply chain programs to safeguard national competitiveness.<\/p>\n\n\n\n

In short, the surge in AI computing demand has transcended the realm of technology\u2014it has become a central driving force reshaping global supply chains and geopolitical dynamics.<\/p>\n\n\n\n

<\/p>\n\n\n\n

II. Strategic Implications of the Scale-Up Technological Transformation<\/h4>\n\n\n\n

<\/p>\n\n\n\n

In AI computing, the essence of Scale-Up technology is not merely stacking more GPUs or accelerators, but minimizing performance loss caused by communication overhead. As model parameters grow larger, parallel computing becomes inevitable. However, in multi-GPU and multi-node environments, excessive interconnect latency can leave processors idle, creating a significant gap between nominal and effective computing power.<\/p>\n\n\n\n

Scale-Up aims to close this gap by enabling higher-bandwidth interconnects within a single node and building low-latency topologies within each server or rack to maximize computational efficiency. For example, NVIDIA\u2019s NVLink and NVSwitch architectures are classic Scale-Up solutions: by tightly coupling multiple GPUs, they allow tensor data transfers during model training to approach near-memory bandwidth. Thus, Scale-Up represents not simply a matter of hardware quantity, but a systemic strategy for optimizing communication efficiency. From a business perspective, this translates into better return on investment (ROI) \u2014 with the same number of GPUs, a more efficient interconnect design yields higher output, creating more computational value under fixed capital expenditure.<\/p>\n\n\n\n

The evolution of Scale-Up technology can be divided into three tiers: intra-node, intra-rack, and inter-rack expansion.<\/p>\n\n\n\n

Intra-node optimization focuses on GPU-to-GPU interconnects and memory coherence mechanisms, requiring ultra-high-speed direct interfaces and shared-memory access protocols. For instance, Rubin CPX chips have explored PCIe 6.0 interconnects to mitigate bandwidth bottlenecks.<\/p>\n\n\n\n

Intra-rack design emphasizes network topology efficiency \u2014 architectures such as Torus or Dragonfly ensure that tensor and pipeline parallelism can be completed with minimal communication latency.<\/p>\n\n\n\n

Inter-rack or cross-data-center scaling must balance Ethernet cost against the performance of proprietary interconnects, while leveraging software stack partitioning to reduce cross-cluster synchronization delays.<\/p>\n\n\n\n

The synergy among these three layers defines the true value of Scale-Up. Without high-speed intra-node links, models hit the \u201cmemory wall\u201d; without low-diameter rack-level networks, compute resources cannot be efficiently integrated; and without flexible cross-rack architecture, data centers cannot scale sustainably. This multilayer coordination makes Scale-Up a full-stack systems engineering approach spanning hardware design, interconnect topology, and software scheduling.<\/p>\n\n\n\n

Today, Scale-Up development is diverging into two distinct paths:<\/p>\n\n\n\n

Highly integrated proprietary architectures, led by single vendors \u2014 for example, NVIDIA\u2019s ecosystem of NVLink, NVSwitch, and CUDA software stack. This closed-loop model achieves peak performance, ideal for large-scale model training and long-context inference, yet it entails high cost and deep supply-chain dependence.<\/p>\n\n\n\n

Open modular designs, emphasizing interoperability across vendors \u2014 including standards like PCIe 6.0, CXL memory pooling, and the emerging UALink. These allow accelerators from different suppliers to interconnect via common protocols, enhancing flexibility and supply-chain resilience.<\/p>\n\n\n\n

While open architectures may not always match the raw performance of proprietary systems, they deliver better cost efficiency and long-term operability in large-scale inference and multi-tenant enterprise environments. In practice, the two approaches are complementary, addressing different market segments. For data-center operators, the competitive advantage lies in managing both architectures concurrently and abstracting their differences through software.<\/p>\n\n\n\n

Strategically, the importance of Scale-Up extends beyond performance gains \u2014 it redefines the Total Cost of Ownership (TCO) equation. In traditional IT investment, hardware acquisition dominated capital expenditure. In the AI era, however, energy consumption, cooling, and maintenance costs are rising rapidly, often surpassing the hardware itself. By reducing interconnect latency and improving compute efficiency, Scale-Up architectures deliver more usable computation per watt, thereby lowering the energy cost per unit of performance.<\/p>\n\n\n\n

As rack-level power density increases, liquid and immersion cooling become standard. Without offsetting these investments through higher compute efficiency, data-center TCO would deteriorate rapidly. Thus, Scale-Up is not only a technical choice but also a financial and operational strategy. The ability to balance performance with cost determines the adoption rate and scalability of these systems.<\/p>\n\n\n\n

From a strategic and geopolitical standpoint, Scale-Up has become a critical battleground in the global technology supply chain.<\/p>\n\n\n\n

For upstream semiconductor suppliers, the demand for high-bandwidth memory (HBM), advanced packaging, and large-format substrates makes these components the new scarce resources defining next-generation computing capacity.<\/p>\n\n\n\n

For midstream system integrators, competitive advantage depends on integrating interconnect, power, and cooling into rack-scale solutions while optimizing delivery time and operational cost.<\/p>\n\n\n\n

For downstream cloud and AI service providers, Scale-Up choices directly impact their ability to deliver large-scale inference at a sustainable cost \u2014 shaping long-term business viability.<\/p>\n\n\n\n

On a broader scale, Scale-Up is tightly linked to computing sovereignty. A nation lacking access to proprietary interconnect or advanced packaging cannot maintain parity in AI competitiveness, even with domestically developed GPUs. Hence, Scale-Up is not merely a technological milestone \u2014 it is a strategic lever in the global rebalancing of the AI supply chain.<\/p>\n\n\n\n

III. Global Competitive Landscape and the Reshuffling of the Industrial Chain<\/h4>\n\n\n\n

In the past, competition in the AI accelerator market centered primarily on single-card computing performance\u2014whoever could deliver the highest FLOPS or TOPS would attract the majority of cloud service providers. However, as model scales expanded dramatically and cross-node parallelism became more complex, the importance of single-chip performance has declined. The new battlefield now lies in system-level performance, which encompasses three key dimensions:<\/p>\n\n\n\n

Inter-GPU communication efficiency, determining the overhead of tensor partitioning and parameter synchronization.<\/p>\n\n\n\n

Switch network and optical module latency and power consumption, influencing large-scale inference throughput.<\/p>\n\n\n\n

Software stack maturity, including the optimization of deep learning frameworks, distributed compilers, and communication libraries\u2014all of which directly affect how effectively hardware can deliver its nominal performance.<\/p>\n\n\n\n

In other words, today\u2019s competition is no longer about simply \u201cstacking hardware,\u201d but rather about hardware\u2013software co-design and full-stack optimization. The ability to minimize latency and maximize system utilization efficiency has become the decisive factor in both corporate and national competitiveness in AI computing power.<\/p>\n\n\n\n

The market has now evolved into a dual-structure competition between proprietary ecosystems and open standards. The former is led by NVIDIA and AMD, leveraging proprietary interconnects such as NVLink and Infinity Fabric, combined with software stacks like CUDA and ROCm, to form highly integrated closed systems. This model excels in performance optimization, ideally suited for cutting-edge model training. However, it also creates deep supplier dependence, high costs, and limited bargaining flexibility.<\/p>\n\n\n\n

Conversely, open standards\u2014such as PCIe 6.0, CXL, UALink, and SUE (Scalable Ultra-Ethernet)\u2014are maturing, allowing multi-vendor hardware interoperability. Although open architectures may temporarily lag proprietary ecosystems in peak performance, they offer clear advantages in cost efficiency, flexibility, and sustainability. Particularly during inference stages, where enterprises and multi-tenant workloads dominate, open ecosystems are preferred for reducing supply-chain risk and improving long-term ROI. As a result, the global market is now characterized by the coexistence of high-performance proprietary clusters and large-scale open networks, with the balance of power shifting dynamically across different application scenarios.<\/p>\n\n\n\n

Major cloud service providers (CSPs)\u2014including Google, Amazon, Microsoft, and Meta\u2014are aggressively developing in-house accelerators, not only to reduce dependence on single suppliers but also to control costs and differentiate their services. Examples include Google\u2019s TPU and Amazon\u2019s Trainium\/Inferentia, both custom-designed for specific workloads.<\/p>\n\n\n\n

This wave of in-house chip development is reshaping the industry value chain: profits once concentrated in GPU manufacturers are now shifting toward design services, foundries, OSAT (Outsourced Semiconductor Assembly and Testing), and substrate suppliers. In particular, the supply chains for advanced packaging and HBM (High Bandwidth Memory) have become the new bottleneck resources. CSPs working on proprietary chips are forming closer partnerships with foundries such as TSMC and Samsung, while treating advanced packaging technologies like CoWoS and SoIC as strategic production assets. Consequently, upstream suppliers are gaining influence, while traditional server OEMs\/ODMs must transform into system integrators, offering value-added services such as liquid cooling, rack-level delivery, and software optimization to maintain relevance within the value chain.<\/p>\n\n\n\n

The global computing power race is not purely a market phenomenon\u2014it is heavily influenced by geopolitics. The United States\u2019 restrictions on high-end GPU exports to China have forced Chinese companies to accelerate domestic AI accelerator and interconnect architecture development. Meanwhile, the EU, Japan, and South Korea are advancing their own computing sovereignty initiatives to ensure independence over strategic resources.<\/p>\n\n\n\n

These policies have significantly reshaped supply and demand, with long-term implications for the global value chain. For instance, export restrictions on advanced process nodes compel Chinese firms to adopt legacy nodes and alternative architectures, forming a \u201clower-performance but sustainable\u201d strategy. The EU, on the other hand, is investing in supercomputing centers and semiconductor alliances to gain influence in open-standard ecosystems. Computing power competition has thus transcended business\u2014it has evolved into a national-level industrial security race. Over the next several years, computing sovereignty and supply-chain resilience will become critical investment criteria for both governments and enterprises, likely triggering a new wave of technological divergence and market barriers.<\/p>\n\n\n\n

With these shifts in competition and policy intervention, the global AI industrial chain is undergoing profound restructuring.<\/p>\n\n\n\n

High-margin, high-barrier segments such as HBM memory, advanced packaging, optical modules, and switch ICs have become new focal points of value.<\/p>\n\n\n\n

Infrastructure layers\u2014including liquid cooling, power distribution, and monitoring software\u2014are evolving from supportive utilities into core differentiators.<\/p>\n\n\n\n

This means the industry\u2019s value focus is moving from individual accelerator performance to system integration and supply-chain collaboration. In this new paradigm, companies capable of providing end-to-end solutions\u2014those integrating packaging, cooling, and software abstraction\u2014will hold greater bargaining power than those limited to single-component supply.<\/p>\n\n\n\n

Overall, global competition is shifting from point-based performance battles to multi-layered ecosystem competition. In this reshuffling, technical capability and supply-chain control will be equally critical\u2014and those who lead in both dimensions will command the next five to ten years of the AI computing power race.<\/p>\n\n\n\n

IV. China\u2019s Unique Development Path in AI Computing<\/h4>\n\n\n\n

Due to U.S. export restrictions on high-end GPUs and advanced-process chips, the first defining characteristic of China\u2019s AI computing development is \u201cavailability first.\u201d This means that Chinese enterprises and research institutions prioritize supply-chain stability and delivery sustainability over maintaining absolute performance parity with global leaders.<\/p>\n\n\n\n

Historically, high-end AI training clusters relied heavily on NVIDIA\u2019s A100\/H100 series or AMD\u2019s MI300 accelerators. Under current restrictions, however, Chinese companies have accelerated adoption of domestically developed GPUs (such as Huawei Ascend, Biren Technology, and Cambricon) and alternative solutions such as FPGAs and ASICs. Although these domestic chips generally lag behind international peers in raw performance and energy efficiency, their accessibility allows continuous infrastructure development, preventing China\u2019s computing capacity from stagnating.<\/p>\n\n\n\n

Moreover, this \u201cavailability-first\u201d strategy encourages Chinese vendors to rapidly develop software stacks and ecosystem adaptations, narrowing the performance gap through software optimization. Over time, this has enabled them to build differentiated advantages in vertical applications, where integration and customization matter more than peak performance.<\/p>\n\n\n\n

Another distinctive trajectory of China\u2019s AI computing architecture lies in its hybrid Scale-Up and Scale-Out systems built around domestic accelerators, Ethernet-based interconnects, and select proprietary links. Because self-developed GPUs and accelerators have yet to reach international performance levels, many Chinese vendors rely on Ethernet as the backbone for inter-cluster communication, taking advantage of its maturity, cost control, and well-established local supply chain to scale inference clusters efficiently.<\/p>\n\n\n\n

Meanwhile, solutions such as Huawei\u2019s Ascend CANN and Inspur\u2019s AI servers employ proprietary high-speed interconnects within local clusters to minimize card-to-card latency and enhance training efficiency. This \u201cintra-island proprietary + inter-island Ethernet\u201d model enables China to construct a functional AI infrastructure under constrained conditions.<\/p>\n\n\n\n

As a result, China\u2019s data centers are highly heterogeneous\u2014different generations of GPUs and varying interconnect standards often coexist. While this complexity increases operational and maintenance burdens, it has also driven major investment in software abstraction, orchestration, and resource scheduling, creating unique technological know-how and resilience.<\/p>\n\n\n\n

Unlike the U.S. market, which focuses heavily on building large training clusters, China emphasizes rapid deployment of inference environments. The reason is that both Chinese enterprises and government agencies derive AI demand primarily from application-driven scenarios\u2014such as intelligent customer service, financial risk management, education support, and government data analytics.<\/p>\n\n\n\n

These applications do not require cutting-edge training capabilities but demand sustained, large-scale inference services that can operate under high concurrency at reasonable cost. Consequently, China\u2019s data-center investments lean toward massive inference cluster deployment.<\/p>\n\n\n\n

This strategy has proven effective in several ways:<\/p>\n\n\n\n

On one hand, inference adoption in China has accelerated dramatically, allowing AI to permeate both consumer and enterprise markets at record speed.<\/p>\n\n\n\n

On the other hand, domestic accelerator vendors have leveraged scale-based advantages\u2014even if single-card performance remains limited, large-scale deployment can still meet real-world requirements.<\/p>\n\n\n\n

This application-driven computing model enables China to achieve faster AI commercialization compared to Western markets.<\/p>\n\n\n\n

The Chinese government views computing power as a national strategic resource, advancing initiatives such as \u201cEastern Data, Western Computing\u201d (\u4e1c\u6570\u897f\u7b97) and national supercomputing centers. These projects address the imbalance of data-center distribution and energy consumption while promoting greener, more sustainable AI computing.<\/p>\n\n\n\n

By establishing supercomputing hubs in energy-rich western regions, China seeks to combine computing infrastructure with renewable energy (\u201ccomputing hubs + green power\u201d), reducing overall TCO (Total Cost of Ownership) and easing power and cooling constraints in eastern metropolitan areas.<\/p>\n\n\n\n

Simultaneously, the government promotes open computing platforms, encouraging enterprises and research institutes to share computing resources to improve utilization rates. These measures not only help sustain China\u2019s computing growth under hardware constraints but also nurture the domestic software stack and application platforms, allowing them to adapt more quickly to diverse needs.<\/p>\n\n\n\n

The combination of policy direction and infrastructure development has given China\u2019s AI ecosystem a distinct \u201csystems engineering\u201d character\u2014contrasting with the technology-driven model of the United States.<\/p>\n\n\n\n

Although China\u2019s AI computing trajectory currently lags behind the U.S. and Europe in raw performance and technological sophistication, it demonstrates unique strategic value:<\/p>\n\n\n\n

Availability-first ensures uninterrupted computing progress despite global supply constraints.<\/p>\n\n\n\n

Inference-first drives rapid application deployment, enabling tangible AI value creation in finance, healthcare, education, and public governance.<\/p>\n\n\n\n

Hybrid architectures foster expertise in software orchestration and resource management, which may evolve into a long-term competitive edge.<\/p>\n\n\n\n

Policy support and green energy strategies balance economic efficiency with sustainability.<\/p>\n\n\n\n

Overall, China\u2019s AI development model is pragmatic rather than perfectionist\u2014it may not dominate global performance benchmarks but can meet vast market demands swiftly while accumulating indigenous technical capabilities.<\/p>\n\n\n\n

This path represents a strategic alternative in the global computing race\u2014an application-driven, progressively self-reliant ecosystem\u2014which will profoundly influence the structure of international supply chains and competitive dynamics in the next five to ten years.<\/p>\n\n\n\n

V. Reassessing the Demand for Switch ICs and Foundational Components<\/h4>\n\n\n\n

Within the architecture of AI data centers, the Switch IC (switching chip) has evolved from a peripheral component into a core determinant of overall system performance. As AI models continue to grow in scale, the computational power of a single server or rack is no longer sufficient for training and inference workloads. Cross-node and cross-cluster interconnection has become indispensable.<\/p>\n\n\n\n

In this context, the transmission rate, latency, power efficiency, and programmability of switch chips directly define system throughput and ROI. For instance, whether 800G switching can reliably support AI clusters with hundreds or even thousands of nodes will determine the feasibility of large-scale inference networks. When switching performance becomes a bottleneck, even an abundance of GPUs cannot prevent network-induced compute waste. As such, Switch ICs have become the \u201cvalves\u201d of AI computing infrastructure\u2014those who can balance energy efficiency and cost will gain dominance as the supply chain undergoes restructuring.<\/p>\n\n\n\n

AI workloads impose fundamentally different demands on switching chips compared to traditional cloud networks. Conventional network traffic is typically balanced east\u2013west and north\u2013south, with higher latency tolerance. In contrast, AI training and inference require ultra-low-latency tensor communication and massive parameter synchronization, where energy consumption per bit becomes a critical constraint.<\/p>\n\n\n\n

The design challenges can be summarized in three dimensions:<\/p>\n\n\n\n

Speed scaling: Switch chips are moving from 400G to 800G and 1.6T, with 3.2T already on the horizon.<\/p>\n\n\n\n

Power efficiency: As data center power consumption surges, excessive per-port power draw directly undermines total cost of ownership (TCO).<\/p>\n\n\n\n

Programmability: AI traffic patterns differ from conventional workloads, requiring fine-grained scheduling and congestion control at the switch level.<\/p>\n\n\n\n

To address these challenges, chip designers are rapidly migrating to advanced process nodes (7nm \u2192 5nm \u2192 3nm) and combining SerDes circuit innovation with DSP algorithm optimization to balance throughput and power efficiency.<\/p>\n\n\n\n

As AI clusters scale, traditional copper cables and optical modules can no longer meet high-bandwidth requirements\u2014leading to a shift toward opto-electronic integration. Technologies such as CPO (Co-Packaged Optics) and NEO (Near-Packaged Optics), which deeply integrate optical modules with switch ICs, are emerging as critical enablers for reducing transmission latency and front-panel power consumption.<\/p>\n\n\n\n

For AI data centers, CPO is not merely a performance enhancement\u2014it is an inevitable path for energy efficiency optimization. As 800G transitions toward 1.6T, the energy-efficiency ceiling of discrete optical modules becomes increasingly evident, making co-packaged optics indispensable. This convergence blurs the traditional boundary between switch chips and optical components; the next stage of competition will extend from chip design to holistic \u201cswitch + photonics\u201d systems.<\/p>\n\n\n\n

Taiwan and South Korea are particularly well-positioned in this evolution\u2014their opto-electronic component ecosystems and silicon photonics suppliers are expected to benefit from the structural growth driven by CPO adoption.<\/p>\n\n\n\n

According to industry projections, the next three years will see double-digit annual growth in demand for high-end switch ICs and optical modules, fueled by the rapid expansion of AI clusters. During the inference phase in particular, the rise of multi-tenant and real-time interactive applications will sustain strong demand for large-scale, low-latency networks.<\/p>\n\n\n\n

However, several supply-side bottlenecks could constrain this growth:<\/p>\n\n\n\n

Limited advanced-node capacity, as both switch ICs and high-performance GPUs rely on a small number of foundries such as TSMC.<\/p>\n\n\n\n

High packaging and testing complexity in opto-electronic integration, potentially leading to low yields and high costs in the short term.<\/p>\n\n\n\n

Rising cooling and power requirements, further inflating capital expenditure for data center operators.<\/p>\n\n\n\n

Thus, while demand growth is certain, the market will likely face persistent supply shortages and elevated costs. For investors and manufacturers alike, early capacity planning and long-term partnerships will be crucial for capturing value in this rapidly expanding segment.<\/p>\n\n\n\n

For Taiwan and South Korea, this revaluation of Switch IC and foundational component demand presents both unprecedented opportunities and formidable challenges.<\/p>\n\n\n\n

Taiwan\u2019s strengths in IC design, packaging\/testing, and optical module manufacturing\u2014exemplified by firms like MediaTek and Realtek in switch-chip design, and TSMC\u2019s leadership in advanced process technology\u2014give it a strong first-mover advantage. South Korea, leveraging Samsung\u2019s capabilities in memory and advanced packaging as well as its optical component ecosystem, also stands to benefit from this wave of AI infrastructure investment.<\/p>\n\n\n\n

The key challenge for both regions, however, lies in breaking the technological dominance of global giants and gaining influence in emerging international standards. Furthermore, establishing long-term partnerships with U.S. and European CSPs to secure stable orders and capacity allocation will determine whether Taiwanese and Korean firms can fully capitalize on this surge in AI infrastructure investment.<\/p>\n\n\n\n

In summary, the reassessment of Switch IC and opto-electronic integration demand is not merely a technological upgrade, but a strategic redistribution of value across the global supply chain\u2014reshaping the industrial landscape for the AI era.<\/p>\n\n\n\n

VI. Expert Insights: Strategic Choices for the Next Five Years<\/h4>\n\n\n\n

Technical experts broadly agree that the core challenge over the next five years will not be about the peak performance of a single GPU or accelerator, but rather about system-level throughput efficiency and energy performance (performance per watt). As AI model sizes continue to expand, improvements in single-card performance are no longer sufficient to offset the synchronization latency across multiple nodes. This means that hardware architecture design must increasingly emphasize interconnect bandwidth, memory pooling, and network topology optimization to maximize GPU cluster efficiency.<\/p>\n\n\n\n

At the same time, power consumption has become the defining bottleneck of data centers. Future technology roadmaps will prioritize throughput per watt over raw FLOPs. Over the next five years, CXL, PCIe 6.0, HBM4, and liquid-cooling technologies will serve as key levers for overcoming these constraints. Experts recommend that enterprises establish dual-track R&D models\u2014developing both performance and energy-consumption models\u2014and align algorithm partitioning with hardware interconnect design to maintain competitiveness in a rapidly shifting market.<\/p>\n\n\n\n

Industry analysts note that the AI computing power supply chain is entering a phase of value-chain redistribution. Historically, most profits were concentrated in GPU manufacturers, but as interconnect, optical modules, advanced packaging, and cooling technologies rise in importance, value creation is shifting toward these high-margin bottleneck segments.<\/p>\n\n\n\n

Among them, HBM memory and optical modules stand out as critical profit pools due to their supply constraints and high gross margins, giving suppliers in these fields stronger bargaining power. Meanwhile, system integrators are also gaining strategic significance\u2014the ability to integrate power delivery, cooling, and software abstraction within rack-level solutions will directly influence market standing.<\/p>\n\n\n\n

Industry experts emphasize that over the next five years, companies relying solely on narrow technical advantages without cross-domain integration will gradually lose competitiveness. Conversely, those that can deliver end-to-end system solutions\u2014spanning hardware, software, and infrastructure\u2014will emerge as dominant players in the new industry structure. Strategically, this implies that cross-domain collaboration and systems integration capability are now more valuable than merely pursuing hardware supremacy.<\/p>\n\n\n\n

From a policy perspective, experts identify energy, supply chain resilience, and computing security as the three top national priorities.<\/p>\n\n\n\n

Energy constraints are becoming acute as AI data-center rack densities soar\u2014racks exceeding 80 kW are increasingly common\u2014forcing governments to address how to maintain computational growth within limited grid capacity.<\/p>\n\n\n\n

Supply-chain security, heightened by geopolitical risks, now sits at the core of national strategy. Mastery of advanced process nodes, HBM, advanced packaging, and optical components will determine whether nations can sustain computing sovereignty.<\/p>\n\n\n\n

Computing power itself is being recognized as a strategic resource. Governments worldwide are establishing supercomputing centers and computing hubs, and promoting data-as-an-economic-asset policies to ensure equitable access and national competitiveness.<\/p>\n\n\n\n

Policy experts advise that governments, over the next five years, should focus on three pillars: energy pricing reform, environmental standards, and supply-chain investment. This would ensure that computing development and economic security progress in tandem\u2014affecting not only the tech sector but also the financial, manufacturing, and governance spheres.<\/p>\n\n\n\n

Investment experts identify two key strategic indicators for decision-making in the next five years: energy-efficiency hubs and delivery moats.<\/p>\n\n\n\n

Energy-efficiency hubs refer to companies that consistently lead in throughput per watt and throughput per rack, allowing them to mitigate the risks of rising energy costs and tighter regulatory approvals while maintaining growth.<\/p>\n\n\n\n

Delivery moats describe companies capable of shortening production and delivery cycles and ensuring supply stability within complex global supply chains\u2014for example, system integrators that can seamlessly integrate liquid cooling, power systems, optical modules, and software orchestration.<\/p>\n\n\n\n

As market demand accelerates, the ability to guarantee delivery timelines and product quality will become more valuable than sheer technical specifications. Investors are thus advised to prioritize companies that combine technological advancement, superior energy efficiency, and resilient delivery capabilities, particularly in the fields of HBM, Switch ICs, optical modules, and liquid cooling.<\/p>\n\n\n\n

Integrated Expert Perspectives: Hybrid Paths and Long-Term Resilience<\/p>\n\n\n\n

Synthesizing insights across disciplines, the strategic roadmap for the next five years will follow a dual trajectory characterized by \u201chybrid pathways\u201d and \u201clong-term resilience.\u201d<\/p>\n\n\n\n

The hybrid pathway implies that enterprises and governments will not bet on a single technological direction but instead pursue both proprietary, highly integrated architectures and open-standard ecosystems to accommodate diverse application needs.<\/p>\n\n\n\n

Long-term resilience emphasizes supply-chain diversification, energy-efficiency improvement, and software ecosystem stability, all of which determine whether industries can sustain growth amid volatility.<\/p>\n\n\n\n

Experts converge on one key conclusion: the winners of the future will not necessarily be those with the strongest technology, but those that can strike the optimal balance among performance, cost, energy efficiency, delivery stability, and regulatory compliance.<\/p>\n\n\n\n

This perspective encapsulates the true nature of the AI industry\u2019s evolution\u2014it is no longer a race of isolated breakthroughs, but a long-term, full-stack contest built upon system-level coordination and cross-domain integration.<\/p>\n\n\n\n

Conclusion and Recommendations<\/p>\n\n\n\n

Across all six preceding sections, it is clear that AI computing power has evolved beyond a mere technological or commercial issue\u2014it has become a strategic resource at the core of global industry and geopolitics. The scaling of AI models has reshaped the structure of computing demand, redefining the architecture of data centers around dual needs for training and inference.<\/p>\n\n\n\n

Scale-Up and Scale-Out are no longer mutually exclusive but complementary, forming a new equilibrium of \u201cintra-cluster extreme performance + inter-cluster flexible scalability.\u201d Global competition has thus shifted from hardware specifications to a battle over full-stack integration, energy efficiency, and delivery speed.<\/p>\n\n\n\n

China, meanwhile, demonstrates a distinct path characterized by \u201cavailability-first\u201d and \u201cinference-driven\u201d strategies, highlighting how policy and application orientation can profoundly shape industry trajectories.<\/p>\n\n\n\n

From an expert perspective, the next five years will not be defined by single-point breakthroughs, but by the ability to balance performance, cost, energy efficiency, supply-chain security, and policy compliance.<\/p>\n\n\n\n

For System and Server Vendors:
Future competition will hinge not merely on hardware design but on rack-scale and data-center-level delivery capabilities. Vendors should adopt dual-path product strategies\u2014supporting both highly integrated proprietary interconnect architectures for large-scale model training and open-standard architectures for multi-tenant inference workloads.<\/p>\n\n\n\n

Rack-scale delivery and liquid cooling must become standard features; without them, competitiveness will erode in high-density environments. Moreover, vendors should strengthen software abstraction and orchestration capabilities\u2014such as unified cluster management and performance monitoring platforms\u2014to reduce customer costs across heterogeneous infrastructures. This integration capability will serve as the true moat in the next-generation AI market.<\/p>\n\n\n\n

For Semiconductor and Component Suppliers:
Priority should be placed on next-generation bottleneck components, especially HBM memory, advanced packaging, Switch ICs, and optical modules. R&D should focus on two pillars: energy efficiency and manufacturability.<\/p>\n\n\n\n

Improving energy efficiency per bit will become a key procurement metric\u2014any design neglecting power efficiency risks obsolescence within three to five years. Meanwhile, manufacturability and yield rates will determine scalability, particularly for HBM and CPO (Co-Packaged Optics) technologies.<\/p>\n\n\n\n

Suppliers should pursue long-term partnerships with CSPs and system integrators to secure capacity allocation and safeguard competitiveness during supply-chain constraints. For Taiwanese and Korean manufacturers, this represents a golden window\u2014those who can secure positions in standard-setting bodies and international collaborations may advance from contract manufacturers to strategic partners.<\/p>\n\n\n\n

For Data Center Operators and CSPs:
The foremost challenge will be achieving sustainability across power, cooling, and networking. Operators should treat liquid and immersion cooling as core competencies, not outsourced services, and establish standardized platforms for energy and cooling management to enhance long-term PUE (Power Usage Effectiveness) and WUE (Water Usage Effectiveness) competitiveness.<\/p>\n\n\n\n

Networks must rapidly evolve toward 800G and 1.6T programmable switching architectures to accommodate AI-specific traffic patterns. CSPs, meanwhile, should adopt dual strategies\u2014developing proprietary accelerators while embracing open standards\u2014to maintain technological autonomy while avoiding vendor lock-in.<\/p>\n\n\n\n

Finally, data-center operators should integrate renewable energy contracts and energy storage systems into their TCO management. Leadership in energy and infrastructure strategy will directly determine the sustainable scalability of AI businesses.<\/p>\n\n\n\n

For Policymakers:
Given AI computing\u2019s strategic importance, government<\/p>\n\n\n\n

Table 1: Evolution of AI Computing Characteristics and Data Center Impact<\/h4>\n<\/div>\n\n\n\n\n\n\n\n\n\n
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