During the first half of 2026, surging demand for KV Cache coupled with constrained memory supply resulted in severe memory bottlenecks. To resolve the KV Cache bottlenecks, industry players are seeking solutions from both the supply side of KV Cache-addressable memory capacity and the demand side. Regarding the expansion of the addressable memory capacity, Penguin Solutions launched the MemoryAI™ KV Cache Server, Marvell introduced the Structera S CXL switch, and Meta developed its proprietary Vistara CXL switch to expand the memory hierarchy. On the demand side, NVIDIA introduced KVTC, and Google launched TurboQuant to compress the KV Cache.
This report provides an in-depth analysis of: (1) the KV Cache bottleneck; (2) expanding available KV Cache capacity through CXL and KV Cache offloading; (3) reducing KV Cache capacity demand via attention mechanisms and KV Cache quantization; (4) methods for improving decode efficiency, specifically MTP and DiffusionGemma; and (5) the broader impact on the memory market. The objective is to evaluate the technical principles, performance metrics, and future development trajectories of various KV Cache debottlenecking technologies.
The global humanoid robotics industry is advancing rapidly across three key hubs—China, North America, and the broader Asian supply chain—and is approaching a pivotal inflection point around 2026 as the industry transitions from technology validation to early commercialization.
The evolution of the ecosystem showcased at EAI SHOW, the growing pressure for commercial deployment highlighted at the Robotics Summit, and the advances in supply chain capabilities and computing platforms demonstrated at COMPUTEX all indicate that industry competition is shifting away from isolated technological breakthroughs toward system-level competition encompassing AI foundation models, critical components, and standard ecosystems.
Against this backdrop, humanoid robots are no longer viewed merely as an extension of industrial automation. Instead, they are increasingly emerging as the primary embodiment of next-gen Physical AI and a key catalyst for industry-wide transformation.
The need for power integrity in AI servers is extending from the board level directly into the package. This shift is driving a transition in capacitor technology, moving beyond a sole reliance on traditional multi-layer ceramic capacitors (MLCCs) toward a layered, complementary approach utilizing both silicon capacitors and MLCCs. While MLCCs remain the primary components for system-level decoupling, filtering, and voltage regulation across PCBs, VRMs, power shelves, and power modules, silicon capacitors offer distinct localized advantages. Their thin profile, low equivalent series inductance (ESL), excellent high-frequency characteristics, and stable capacitance under DC bias and temperature fluctuations make them ideal for near-die decoupling around GPUs, ASICs, HBMs, and within advanced packages. As AI accelerators increasingly adopt chiplets, HBM stacking, and high-power packaging, silicon capacitors are poised to become vital complementary components for package-level power integrity in AI and HPC applications.
On February 16th 2026, MediaTek CEO Rick Tsai stated in his ISSCC 2026 keynote that the basic unit for measuring AI compute efficiency will no longer be just the AI chip, but the entire AI system, including interconnect architecture, power, and cooling. On May 4th 2026, GUC likewise announced a strategic technology partnership with Wiwynn to jointly develop rack/pod‑scale solutions, indicating that competition among AI chip design houses is expanding from the chip scale to the rack/pod scale.
This report therefore focuses on an in‑depth analysis of: (1) NVIDIA’s AI rack/pod‑scale strategy, (2) the rack/pod‑scale AI deployments of major CSPs, and (3) rack/pod‑scale solutions from ASIC design and service providers. The goal is to clarify each vendor’s current AI rack/pod‑scale layout and future trends, and to examine the competitive strategies of Taiwanese ASIC design and service providers.
Intensifying global geopolitical conflicts are driving up defense spending, and warfare is pivoting toward asymmetric and information warfare, making low-cost unmanned vehicles (UAVs) crucial. China is deepening military-civilian integration to break through technology controls, while Taiwan is fully developing localized UAV and AI defense supply chains to strengthen resilience.
On March 16th 2026, NVIDIA used GTC to debut the first standalone Vera CPU Rack for sale. On March 25th, Arm announced its own Arm AGI CPU and introduced two CPU rack variants (air‑cooling and liquid‑cooling), formally entering the in‑house CPU market. These moves show that CPUs are becoming increasingly critical in AI data centers, and that overall demand is undergoing a structural shift. Recently, tight CPU supply, together with news that Intel and AMD are both raising prices at the end of 1Q26, has become a key focus for the market.
This report provides an in‑depth analysis of: (1) the importance of CPUs in Agentic AI applications, (2) the competitive landscape of the CPU market in 2026, and (3) business opportunities in back‑end CPU design services. The goal is to clarify CPU demand drivers in the era of rapidly growing Agentic AI, competitive dynamics among vendors, as well as newly emerging potential opportunities.
NVIDIA once again showcased its Rubin-series chips and rack systems—scheduled for launch by the end of 2026—at GTC 2026 on March 16th. Compared to the previous generation, the Rubin series boasts significantly larger substrate sizes, higher layer counts, and increased rack board complexity.
Furthermore, the shift toward cableless design architectures is driving demand for components such as midplanes and orthogonal backplanes, while the introduction of the inference-focused Rubin LPX rack is further boosting demand for high-end glass fiber cloth.
However, the supply side is facing severe constraints. Nittobo, which controls approximately 90% of the global T-glass market and 60–70% of the NER-glass market, is not expected to bring new capacity online until mid-2027 at the earliest. This implies that supply gaps for critical materials will persist over the next year, with direct implications for lead times and cost trends across the AI server supply chain. This issue warrants close attention from all industry stakeholders.
This report provides an in-depth analysis of:
(1) The technology background and development of glass fiber cloth;
(2) AI-driven demand trends for glass fiber cloth;
(3) Nittobo’s capacity bottlenecks and their impact on pricing.
The objective is to offer a comprehensive view of demand momentum, pricing trends, and competitive dynamics in the high-end fiberglass cloth market.
As data centers adopt HVDC architectures to reduce grid-to-chip power losses, demand for advanced power conversion and next-gen semiconductors surges. Meanwhile, Asian manufacturers continue to dominate the PCB and specialized substrate supply chains.
AI chips represent the core cost component of computing infrastructure. Recently, reports that Google’s TPU has been adopted by several leading companies have drawn attention to the possibility of more cost-efficient alternatives to GPUs.
This report focuses on Google’s TPU, analyzing the long-term demand drivers behind its development within the broader AI arms race. It also examines the product’s competitive advantages and identifies supply chain participants that may benefit from its growing adoption.
As AI compute demand grows exponentially, the physical limits of traditional silicon-based chips are rapidly becoming apparent. The world is accelerating into a disruptive “post-GPU era,” in which quantum computing is no longer confined to laboratory theory but is emerging as a double-edged force capable of reshaping drug discovery and financial risk management—even triggering a “quantum crisis” by undermining national-level encryption systems.
From NVIDIA’s hybrid-computing strategy to the competing technical roadmaps of IonQ and Rigetti, and from the national-level arms race unfolding across the United States, China, Europe, and Japan, the next decade will be defined by a high-stakes contest for computational supremacy. Against this backdrop, Taiwan’s supply chain faces a defining question: how will it position itself in the era of quantum computing?
