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North American cloud giants are substantially increasing Capex, signaling an investment peak in AI infrastructure over the next two years. Shifting to large-scale, long-term strategies, they are focusing on high-end GPU Racks and accelerating in-house AI ASIC development to secure market leadership and drive rapid AI server growth.
Strong CSP demand is driving server DRAM prices significantly higher, with 4Q25 increases revised upwards. CSPs are proactively securing 2027 supply, incentivizing manufacturers to boost capacity, signaling a sustained upward price trend.
Major suppliers are expanding HBM capacity, with 2025 shipments revised upward. SK hynix leads with 150K TSV capacity, while Micron ramps up its Taiwan facilities. By 2026, all three will begin HBM4 mass production, with Samsung’s shipment growth expected to lead.
AI servers lead growth; cloud build-outs lift ODMs/thermal. AMD ramps; TPU up; shift to L2L cooling. Supply chain heat-up end to end.
HDD shortages and surging AI demand are fueling enterprise SSD scarcity. Suppliers are aggressively raising 4Q25 contract prices over twenty percent, with increases continuing as CSPs prioritize supply.
Tariffs and policy changes kept early demand cautious, but cloud expansion and AI investment have lifted memory demand and pricing, tightening DRAM supply. The DDR5 uptrend is established, with profitability likely to surpass HBM3e; vendors’ capacity allocation and pricing strategies will reshape the market landscape.
AI-driven data centers evolve from single-chip to heterogeneous multi-GPU architectures. High-speed optical interconnects enable scalability, while silicon photonics and co-packaged optics boost bandwidth and energy efficiency amid modular, ecosystem-based competition.
This report show Google and AWS driving strong HBM demand, with capacity expanding across generations; META and Huawei join as large buyers.
Global AI server market stays strong as major cloud providers expand infrastructure and platforms; GPU and self-developed ASIC demand rises amid regional dynamics.
This report analyzes the capital expenditure structure of a typical hyperscale AI data center, breaking down the spending into physical infrastructure, IT computing facilities, and networking equipment. Among these, the investment in computing systems, which are servers, is the most significant. As the proportion of higher-end, higher-power AI servers in new projects continues to rise, the cost share of computing systems is expected to increase further.
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