On the survival predicament of the connector industry:
When large model inference moves from the cloud to the desktop, AI PCs have become the “traffic stars” in the field of technology hardware. NVIDIA’s computing power, Intel’s chips, and Lenovo’s complete machines are all under the spotlight. However, behind these giants with tens of billions of dollars in revenue, a revolution in the underlying hardware architecture is quietly taking place.
If the computing power chip is the “heart” of an AI PC, then the connector is the “blood vessel” that conveys data and power. With local inference and high-load collaboration becoming standard, connectors are undergoing an essential transformation from “general connection” to “computing power-level connection”.
This is not only a competition of performance, but also a hidden opportunity for the domestic supply chain to achieve a “surprise leap forward”.
01
Paradigm Shift
AI PC is Reconfiguring the Hardware “Pipeline”
Li Yiping, from the Shenzhen Connector Association, believes that traditional PCs are “passive computing”, while AI PCs are “active intelligence”. This transformation has put an exponential demand on the hardware transmission efficiency. Chen Bo, the business manager of Youqun Technology, has observed that this change has led to a structural growth in the terminal market, and the core driving force behind this growth is the essential differences between AI PCs and traditional PCs in terms of hardware architecture, function deployment, and even energy consumption design.
According to experts from the Shenzhen Connector Industry Association, the core difference between AI PCs and traditional PCs is first reflected in the hardware architecture. Traditional PCs are centered around the CPU, with the GPU mainly responsible for graphics rendering. The mainstream products require 16GB to 32GB of DDR4/DDR5 memory, use standard PCB boards and standard connectors, and the overall design is based on general computing needs, meeting the requirements of multitasking and regular applications. In contrast, AI PCs feature an additional dedicated NPU and adopt a heterogeneous computing architecture of CPU + GPU + NPU. They have been strengthened in terms of memory, storage, interfaces, cooling, and power supply for AI tasks to achieve efficient and low-power local AI inference. The memory configuration starts at 32GB DDR5, with 64GB + as the standard for high-end models, featuring higher frequencies and greater bandwidth to support the loading of large local models and the parallel execution of multiple AI tasks. The new generation of AI PC products will also upgrade the PCB boards, partially applying M9 boards and dedicated connectors to further optimize data throughput efficiency and reduce transmission latency, laying a solid foundation for the release of computing power.
The difference in hardware architecture directly determines the distinct functional positioning and deployment methods of the two. AI PCs focus on local deployment, with large models directly deployed on the edge, enabling private inference without relying on the cloud, and meeting the low-latency local interaction requirements in scenarios such as voice and image processing; in contrast, the AI capabilities of traditional PCs are highly dependent on cloud invocation.
As the global leader in PCs, Lenovo’s AI PC products now account for over 40% of its notebook offerings. “Lenovo’s market share of over 25% is itself a barometer,” Chen Bo stated directly. “The rapid deployment by leading manufacturers means that the entire chain from storage, display to wireless communication must be equipped with higher-specification high-speed connectors.”
This precisely confirms what experts have emphasized: the chain reaction brought about by hardware differences. In the era of AI PCs, connectors are no longer dispensable plug-in components, but rather key bases that adapt to the GPU core architecture, support local intelligent deployment, and balance high power consumption demands. They are also the core hardware “pipelines” that determine whether computing power can be smoothly and efficiently released, and are driving the reconstruction of the entire PC hardware system.
02
Performance Reconstruction
Four Standards for Defining “Computing Power Level Connectors”
The requirements for connectors in AI PCs have comprehensively surpassed traditional consumer electronics standards, entering the era of computing power-level connectors. High speed, high density, low loss, and high reliability are no longer just concepts but the four major technical thresholds that the entire industry chain must overcome. They are also the core breakthrough points for domestic connector enterprises to enter the AI terminal supply chain.
High-Speed Evolution: As the data volume processed by AI PCs grows exponentially, connector bandwidth is rapidly advancing from PCIe 4.0 to 5.0 and beyond. Chen Bo introduced that the transmission speed of his company’s CAMM2 connector is faster than DDR5, and USB Type-C has achieved high-speed transmission of 40G, fully meeting the data transmission requirements of AI PCs. The industry is committed to breaking through the core technical bottlenecks of PCIe 6.0/7.0. This is not merely a specification update but a challenge to physical limits: in extremely high-frequency environments, how to effectively suppress physical noise and slow down signal attenuation has become the core issue for achieving a qualitative leap in bandwidth.
High Density: AI PCs aim for a slim and lightweight design, but the number of modules carrying computing power inside is increasing. This poses strict space utilization requirements for connectors – they must integrate higher performance in a finer pitch. The CAMm2 connector launched by UQUN Technology is the pinnacle of this trend. Its feature of “one replacing four traditional DDR slots” not only saves a significant amount of PCB space but also boosts capacity, effectively resolving the structural contradiction between the miniaturization of AI PCs and their high computing power.
Photo / Youqun Technology LPDDR5 CAMM2 Connector
Low Loss: NPU local inference is highly sensitive to latency. Any slight signal loss can cause the “intelligence” of AI PCs to become “dull”. Therefore, AI PCs require connectors to maintain an extremely low bit error rate during high-speed transmission. This demands that enterprises achieve dual breakthroughs in core technologies. On one hand, it involves upgrading materials science, such as using low-loss boards. On the other hand, it requires optimizing the design of precise structures to reduce signal loss during transmission from the source.
High Reliability: When AI PCs operate at high computing power, temperature rise and electromagnetic interference are common occurrences. This places extremely high demands on the stability of connectors – they must maintain an absolutely stable transmission curve during long-term high-load operation of the equipment.
03
Track Differentiation
Where is the “Must-Win Territory” for Domestic Manufacturers?
As AI PCs accelerate their penetration, the strategic value of different niche markets has become prominent in this computing power revolution, serving as the core driver for domestic connector manufacturers to seize the market and achieve breakthroughs.
The storage connection track is the “cornerstone” of the AI PC experience. The throughput of memory and SSD directly determines the smoothness of AI model operation and data processing, making it a core track in the layout of connectors. Chen Bo stated that the company’s key focus on high-speed connectors precisely meets the high-speed transmission requirements of storage devices.
The high-speed I/O track serves as the “core hub” for the data transmission of AI PC computing power, carrying high-frequency data interaction between the CPU, GPU, and NPU. It places extremely high demands on the transmission rate and stability of connectors, requiring full compatibility with the PCIe 5.0/6.0 standards. Delixion Electronics has strong technical capabilities in the field of high-speed connectors, with outstanding product performance. It is an important domestic supplier of high-speed connection solutions for AI PCs and enjoys high market recognition.
The wireless and display connection track is an “important closed loop” for the intelligent interaction of AI PCs. With the increasing demand for multi-device collaboration and immersive experiences, the stability and smoothness of real-time transmission have become key. This track not only connects terminal devices with display terminals but also supports the full chain transmission of AI interaction, making it an important direction for domestic manufacturers to complete their product matrix and seize the incremental market.
04
Competitive Strategies
How Can Small and Medium-sized Manufacturers “Break Through from the Flank”?
Facing the first-mover advantages of international giants like Tyco and Amphenol, domestic small and medium-sized connector manufacturers are small in scale and have limited resources, making it difficult for them to compete head-on. So, where lies the survival and development opportunities for these small and medium-sized manufacturers?
Specifically, small and medium-sized manufacturers can achieve a flanking breakthrough through three major strategies:
The first is to create single-point champions, avoiding the red ocean competition of general-purpose products, and focusing on in-depth development in niche markets such as storage connection and high-speed I/O. Differentiation advantages are established through specialization.
Second, it focuses on customization. Relying on a flexible R&D and production system, it takes advantage of the shortcoming of large factories that are “slow to change course”, and precisely meets the individualized demands of original equipment manufacturers (OEMs) with “customized services and rapid response”.
Third, it is to integrate with the domestic ecosystem, closely follow the rising trend of domestic computing power chips, and launch a native connector solution that is compatible with the architecture of domestic chips, taking advantage of the domestic substitution trend to achieve a leapfrog development.
Conclusion
AI has become a core tool driving productivity transformation. We are now fully entering the AI era, and everyone needs to leverage AI to enhance work efficiency. The application scenarios of AI PCs will cover two major directions: the enterprise end and the personal end. They can not only support the intelligent transformation of enterprises but also meet the efficiency demands of individuals in daily office work, creation, and learning. The popularization and growth of AI PCs is an inevitable industrial trend.
Such an anticipated future has brought about a rapid development of AI PCs and significantly increased the value of connectors, which can be regarded as the “blood vessels of computing power”. This is not merely an iteration of hardware but also a shift in the industry’s discourse power.
From general connectivity to computing power-level connectivity, Chinese enterprises are now enjoying a historic period of dividends. Perhaps seizing this opportunity is equivalent to grasping the “second growth curve” of the industry.