AI Server Hardware Ecosystem: Market Landscape of Core Components and Future Trends

Computing Chips: Shifts in CPUs and ASICs

• CPU Market Reshaped: In Q1 2025, AMD’s shipment share in the server CPU market hit 50% for the first time, creating a neck-and-neck split with Intel.
  This milestone stems from AMD’s continuous iteration of its EPYC series processors. The 5th-gen Turin platform, launched in 2024, boosted core count to a maximum of 192 cores, delivering significant advantages in AI inference scenarios. When paired with 8 GPUs running large models, the high-frequency EPYC 9575F can 提升 system performance by up to 20%, drastically improving single-machine ROI.
• ARM Architecture on the Rise: In the AI server market, ARM-based CPUs surpassed 25% penetration for the first time, driven by two key use cases: NVIDIA’s GB200 platform uses ARM-based CPU clusters for data preprocessing and distributed training scheduling; meanwhile, cloud providers’ in-house ARM servers—like AWS Graviton4 and Google T2D—offer over 30% better cost-performance than x86 architectures in AI inference, pushing their shipments up 300% year-over-year.
• ASICs Booming: To handle expanding AI workloads and reduce reliance on NVIDIA and AMD, global cloud giants are ramping up investments in ASIC development. In 2025, Amazon AWS’s AI chip shipments are projected to grow over 70%, while Google has launched TPU v6 Trillium, focusing on energy efficiency and optimization for large-scale models.
  In China, local chip suppliers (e.g., Huawei Ascend, Cambricon Thinking Yuan series) are expected to capture 40% of the market in 2025—nearly matching the share of imported chips—backed by national policy support.

Storage Revolution: HBM Dominance and Customization Waves

• HBM Tech Evolution and Market Landscape: High-bandwidth memory (HBM) has become an indispensable core component for AI servers. SK Hynix, leveraging its mass production edge in 12/16-stack HBM3e, commands 64% of the market. Its next-gen HBM4 will deliver over 2TB/s bandwidth, with 40% better energy efficiency and nearly 4% improved heat resistance compared to HBM3e.
  Notably, HBM’s share of total AI server power consumption has risen from 12% a few years ago to 18%—making its efficiency gains critical for reducing overall server energy use.
• Custom HBM as the Future: As AI infrastructure evolves, HBM is shifting from standardized products to customer-specific customization. Choi Jun-yong, head of SK Hynix’s HBM business, notes: “General-purpose HBM products can’t fully meet AI chipmakers’ differentiated needs—customers are showing strong interest in highly customized HBM.”
• Supply-Demand Imbalance and Price Trends: The HBM market faces severe supply shortages. In Q1 2025, HBM prices rose 35% year-over-year, with 16GB HBM3e modules exceeding $1,200.
  SK Hynix predicts this tightness will persist until HBM4 mass production in 2026, with the AI memory market set to grow 30% annually over the next six years. To meet surging demand, SK Hynix boosted HBM production by 70% quarter-over-quarter in Q1 2025, catering to multi-thousand-unit orders from cloud providers like Oracle and AWS.

Networking & Interconnects: Bandwidth Upgrades Driven by Cluster Scales

• Surging Network Bandwidth Demand: As AI cluster sizes expand, demand for network interconnect bandwidth is growing exponentially. In Q1 2025, the high-speed NIC market grew 62% year-over-year, becoming one of the fastest-growing server components.
  In ultra-large AI training clusters, RDMA (Remote Direct Memory Access) technology has cut network latency to microseconds, significantly boosting distributed training efficiency.
• PCIe and CXL Interconnect Upgrades: Next-gen AI servers widely adopt PCIe 5.0, offering up to 32GT/s data transfer rates. AMD’s EPYC 9005 series supports extensive PCIe Gen5 lanes and CXL 2.0, drastically improving data exchange between CPUs and accelerators. Intel’s next-gen CPU platform will also support PCIe 6.0, further pushing data transfer rates to 64GT/s.
• Network Architecture Shifts: To support thousand-GPU AI clusters, InfiniBand and high-speed Ethernet solutions continue to advance. NVIDIA’s Quantum-3 400G InfiniBand switches deliver 400Gb/s per port, slashing training times for 10,000-GPU clusters by weeks. Meanwhile, liquid-cooled network devices are gaining traction, solving heat management issues in high-density setups.

Supporting Components: PCB Upgrades and Material Iteration

• PCBs Evolving for Higher Layers and Frequencies: AI server PCBs are moving toward higher layer counts and high-frequency, high-speed designs. ASIC servers often use rack-based designs, with PCBs modeled after NVIDIA’s Rack architecture, shipped as Compute Trays + Switch Trays.
  Since cloud providers lack NVLink, their Switch Tray PCBs exceed NVIDIA’s specifications. Demand for ASIC server PCBs is projected to surge—from under ¥10 billion in 2024 to nearly ¥60 billion by 2027, accounting for over half of total computing PCB demand.
• Copper-Clad Laminate (CCL) Upgrades: CCL, the core material for PCBs, continues to advance through M6, M7, M8, and M9 grades—with higher grades reducing signal loss.
  NVIDIA currently uses M8 as the primary material in its AI servers. As signal transmission requirements rise, M9 and higher grades are expected to enter use by 2026, driving upgrades in upstream materials like copper foil, glass cloth, and resin.
• Surging Demand for Thermal Materials: As AI server power density rises, the thermal management materials market is booming. NVIDIA’s GB300 server integrates "slot-based modular design + liquid metal interfaces + high-pressure liquid cooling loops," turning liquid cooling from a peripheral aid into a chip-level core solution.
  The liquid cooling market is projected to grow over 100% in 2026 and 50% in 2027, reaching ¥71.6 billion and ¥108.2 billion respectively.

Market Challenges: Supply-Demand Imbalances and Cost Pass-Through

• Spreading Delivery Crises: CPU/MCU lead time indexes spiked 20% in three months, while inventory indexes plummeted 41.6% from December to May. FPGA lead times are even more critical—some AMD UltraScale+ devices face 52-week waits. Supply chain geographic shifts (to avoid tariffs) and onboarding new suppliers are major drivers.
• Stiff Price Increases: Amid supply shortages, key components continue to rise in price. In May 2025, CPU/MCU prices jumped 22.2% month-over-month (largely due to Intel chip shortages), while FPGA prices rose 7.6%. HBM prices climbed 35% year-over-year in Q1 2025, with 16GB HBM3e modules exceeding $1,200.
• Changing Cost Structures: Semiconductors now account for over 75% of AI server costs—up from 45% in traditional servers—with GPUs, HBM, and high-speed interconnect chips making up the bulk. A high-end AI server with 8 NVIDIA H100 GPUs sees HBM alone cost over $10,000, accounting for 15-20% of total machine costs.

Future Trends: Ecosystem Fragmentation and Green Computing

• Hardware Ecosystem Fragmentation: The global AI server hardware ecosystem is splitting along three paths: 1) GPU systems led by NVIDIA + Taiwanese contract manufacturers (e.g., GB200/GB300); 2) CSP-developed ASICs + ODM models (e.g., Google TPU, Amazon Trainium); 3) China’s local alternatives (e.g., Sugon).
  TrendForce predicts China’s AI server market will see local chip suppliers capture 40% in 2025—nearly matching imported chips.
• Rise of Edge Inference Chips: As AI use cases expand, the edge inference chip market is growing rapidly. These chips prioritize energy efficiency and low latency, with wide applications in smart manufacturing and autonomous driving. AMD’s EPYC processors excel in edge inference for sub-10-billion-parameter models—dual EPYC 9965s deliver twice the inference throughput of the previous generation.
• Synergistic Optimization of Computing and Storage: Next-gen AI servers will focus more on coordinating computing and storage capabilities. SK Hynix is developing memory-compute integrated solutions, embedding computing functions into storage units to reduce data movement overhead. AMD is also advancing 3D stacking, integrating HBM with GPUs/CPUs in packaging to boost bandwidth and cut power use.
• Sustainability as a Core Metric: As AI computing demand explodes, green computing has become an industry focus. AMD data shows that in 1,024-node AI inference clusters, ARM servers have a PUE 0.3 lower than x86 solutions, saving 3 million kWh annually. Liquid cooling is also accelerating—projected to exceed 45% penetration in AI data centers by 2027, up 30 percentage points from 2024.