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市場調查報告書
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2020360

全球資料中心運算與人工智慧市場（2027-2040 年）

Computing and AI for Data Centers: Global Market 2027-2040
出版日期: | 出版商: Future Markets, Inc. | 英文 329 Pages, 117 Tables, 133 Figures | 訂單完成後即時交付

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資料中心運算和人工智慧晶片市場涵蓋了用於人工智慧和雲端基礎設施的處理器，包括獨立GPU、客製化AI ASIC、伺服器CPU和資料中心FPGA。自2023年以來，市場規模和結構都發生了變化。 2021年佔據市場主導地位的伺服器CPU，如今僅佔很小一部分。同時，GPU從少數產品躍升至主導地位，這在半導體史上堪稱領頭羊地位最迅速的逆轉。

近期發展主要體現在三個關鍵面向。客製化晶片已從實驗階段邁向量產。谷歌的TPU、AWS的Trainium、Meta的MTIA和微軟的MAIA每年出貨數百萬個加速器，而OpenAI的加速器計畫預計將於2027年開始量產。為此，通用晶片供應商正從銷售單一晶片轉向銷售系統。機架級平台正在湧現，這些平台將72到144個加速器整合在一個統一的連貫中。這種轉變提高了競爭對手的進入門檻，從晶片設計到提供包括電源、液冷和系統軟體在內的整個機架，都面臨挑戰。此外，瓶頸已從晶片轉移到電源。繼2023年出現封裝短缺以及2024年至2025年出現高頻寬記憶體短缺之後，電網連接和電氣設備的前置作業時間現在成為決定產能運作速度的關鍵因素。

展望2040年，預計成長將持續，但成長速度將較2023年至2027年顯著放緩。這一趨勢主要由三項研究發現構成：即使出貨量趨於平穩，收入仍將持續成長。加速器出貨量將在2032年左右達到峰值，但在此期間平均售價將成長五倍以上，導致根據收入趨勢計算出的產能過剩。人工智慧專用積體電路（ASIC）的出貨量將在2028年超過GPU，但營收永遠不會超過GPU。這是因為在低價值推理領域，客製化晶片將取代量產產品，而尖端訓練領域仍將由通用晶片主導。此外，推動需求的成本降低主要來自模型效率和服務軟體的提升，而非製程小型化，這意味著大部分價值創造將在晶片層面實現。

在這個價值鏈中，價值更取決於定位而非執行能力。受限環節——例如最先進的晶圓代工廠、高頻寬記憶體、先進封裝技術以及客製化晶片的協同設計——既蘊含成長潛力，又具備真正的韌性，而系統組裝和通用加速器新興企業則面臨著結構性的經濟困境。風險並非分散，而是集中在特定領域：具體而言，包括電源供應、封裝產能、記憶體供應，以及企業級人工智慧的部署能否按基礎設施建設的計劃從試點階段推進到量產階段。

《資料中心運算與人工智慧—2027-2040年全球市場報告》是一份關於支援全球人工智慧和雲端基礎設施的半導體產業的綜合市場研究報告。該報告涵蓋了美國、台灣、韓國、日本、中國和歐洲的晶片設計公司、晶圓代工廠、記憶體供應商、封裝公司、設備供應商、超大規模資料中心業者資料中心營運商、模型開發公司、系統製造商和基礎設施供應商。

報告包含以下資訊：

  • 執行摘要和市場定義
  • 全球人工智慧基礎設施投資與超大規模資料中心業者資本投資
  • 資料中心電力限制和區域容量擴張
  • 出口限制以及美國與中國之間的技術差距
  • 到2040年處理器市場營收預測：GPU、AI ASIC、伺服器CPU、FPGA
  • 按供應商和項目預測平均售價和銷售量。
  • 晶圓、晶片和先進封裝的需求預測
  • 伺服器托架和機架配置的預測
  • AI 推理和訓練成本以及代幣成本藍圖。
  • 需求推動要素：基於代理的人工智慧、實體人工智慧、建議、編碼、搜尋
  • 資本支出（CAPEX）和營運費用（OPEX）的經濟學分析
  • 生態系、供應鏈以及共同設計者之間的關係圖
  • 按銷售收入、出貨量和安裝量分類的市場佔有率
  • 主要晶片設計公司的財務分析
  • 人工智慧半導體新創企業的資金籌措趨勢
  • 中國當地市場、製造商和供應鏈
  • CPU、GPU 與 AI ASIC 技術及藍圖分析
  • HBM、先進封裝與機架材料清單
  • 新興架構：光電、神經形態學、量子
  • 2040 年之前的看漲、中性和看跌情景、風險和投資前景
  • 81家公司的公司簡介。目標公司包括：Advanced Micro Devices (AMD)、Alchip Technologies、阿里巴巴（T-Head Semiconductor）、Alphawave Semi、Amazon Web Services（Annapurna Labs）、Amkor Technology、Ampere Computing、Analog Devices、Applied Materials、Arm Holdings、ASE Technology、Ampere Computing、Analog Devices、Applied Materials、Arm Holdings、ASE Technology Holding、ASML、Ax、Arenel、Axel） Technology、Broadcom、Cadence Design Systems、Cambricon Technologies、Celestica、Cerebras Systems、Cisco Systems、Coherent Corp、CoreWeave、Credo Technology Group、長信儲存科技（CXMT）、d-Matrix、台達電子、戴爾科技、伊頓公司、Enflame Technology、Etched、富士康（鴻海精密工業）、FuriosaAI、GlobalFoundries、Google（Alphabet）、Groq、環球宇訊科技（GUC）、惠普企業、Hygon Information Technology、Ibiden、Iluvatar CoreX、英飛凌、群創光電、英特爾公司、長電科技集團。 KLA Corporation、Lam Research、Lightmatter、Lumentum Holdings、Marvell Technology、聯發科、Meta Platforms、美光科技、微軟、Monolithic Power Systems、Moore Threads、Nebius Group、英偉達、安森美、OpenAI、Powertech Technology、廣達電腦、Rambus、Rebellions、瑞薩ova Systems、施耐德電機、新光電工業株式會社、SiPearl、SK海力士、中芯國際、超微電腦、Synopsys、Tenstorrent、東京電子、台積電、優尼美光科技、Vertiv Holdings和緯創資通。

目錄

第1章：執行摘要

第2章 市場預測

  • 處理器收入預測
  • 平均售價 (ASP) 預測
  • 處理器銷售量預測
  • 晶圓預測
  • 伺服器托盤容量預測
  • AI伺服器機架架構
  • CPU 焦點
  • 臂燈
  • GPU 和 AI ASIC

第3章 市場趨勢

  • 生成式人工智慧的推理與訓練成本
  • 為什麼會有培訓費用？
  • 從基於代理的人工智慧到實體人工智慧
  • 物理人工智慧
  • 社群網路的推薦模型
  • 編碼助手
  • 搜尋引擎與法學碩士
  • 開爪
  • 生成式人工智慧時代資本支出（CapEx）與營運支出（OpEx）的比較
  • 人工智慧資料中心的未來會是在太空嗎？

第4章 市佔率和供應鏈

  • 資料中心生態系圖
  • 基本模型生態系圖
  • 美中科技戰——時間線
  • 資料中心晶片設計人員的財務指標
  • 人工智慧半導體新創公司資金籌措
  • 案例研究：OpenAI 的收入和吉瓦
  • 市場佔有率：CPU、GPU、AI ASIC 和 XPU 的共同設計者
  • 聚焦中國

第5章 技術分析

  • CPU技術發展趨勢
  • GPU科技發展趨勢
  • AI資料中心流量模式與網路架構
  • 人工智慧專用積體電路技術的發展趨勢
  • GPU與AI ASIC的比較分析
  • 先進的封裝和HBM內存
  • 新運算架構

第6章：展望與情景

  • 市場展望（2026-2040 年）
  • 技術展望（2026-2040）
  • 主要風險和機遇
  • 策略建議

第7章：公司簡介（81家公司簡介）

第8章：報告撰寫與調查方法

第9章：詞彙表和簡稱列表

第10章 參考文獻

The market for computing and AI silicon in data centers covers the processors that do the work inside AI and cloud infrastructure: discrete GPUs, custom AI ASICs, server CPUs and data center FPGAs. What has changed since 2023 is not simply scale but structure. The server CPU, which accounted for the clear majority of this market in 2021, now represents a small fraction of it, while the GPU has moved from a minority position to dominance - the fastest reversal of category leadership in semiconductor history.

Recent activity has been defined by three developments. Custom silicon has moved from experiment to volume: Google's TPU, AWS Trainium, Meta's MTIA and Microsoft's MAIA together now ship millions of accelerators annually, and OpenAI's own programme is expected in volume from 2027. Merchant vendors have responded by selling systems rather than chips, with rack-scale platforms integrating 72 to 144 accelerators behind a single coherent fabric - a shift that raises the barrier to competing from designing a chip to delivering an entire rack, along with its power delivery, liquid cooling and system software. And the binding constraint has migrated from silicon to electricity: after packaging shortages in 2023 and high-bandwidth memory shortages through 2024 and 2025, grid interconnection and electrical equipment lead times now govern how quickly capacity can be commissioned.

The outlook to 2040 is for continued growth at a materially slower rate than the 2023–2027 period. Three findings shape that trajectory. Revenue keeps growing after units stop: accelerator shipments peak around 2032 while average selling prices rise more than five-fold across the period, meaning capacity sized against the revenue curve will be overbuilt. AI ASICs overtake GPUs on unit shipments in 2028 but never on revenue, because custom silicon displaces volume at the lower-value inference end while frontier training remains merchant territory. And the cost reductions driving demand come predominantly from model efficiency and serving software rather than from process scaling - meaning much of the value created accrues above the silicon layer.

Value in this chain is determined by position more than by execution. The constrained layers - leading-edge foundry, high-bandwidth memory, advanced packaging and custom silicon co-design - combine growth with genuine defensibility, while system assembly and the merchant accelerator start-up cohort face structurally weaker economics. Risks are concentrated rather than diffuse: power availability, packaging capacity, memory supply, and whether enterprise AI adoption converts from pilot to production at the rate the buildout assumes.

Computing and AI for Data Centers: Global Market 2027–2040 is a comprehensive market intelligence report on the semiconductors powering global AI and cloud infrastructure. The report covers chip designers, foundries, memory suppliers, packaging houses, equipment vendors, hyperscalers, model developers, systems manufacturers and infrastructure suppliers across the United States, Taiwan, South Korea, Japan, China and Europe.

Report contents include:

  • Executive summary and market definition
  • Global AI infrastructure investment and hyperscaler capital expenditure
  • Data center power constraints and regional capacity buildout
  • Export controls and the US–China technology divide
  • Processor market revenue forecasts to 2040: GPU, AI ASIC, server CPU, FPGA
  • Average selling price and unit volume forecasts by vendor and programme
  • Wafer, die and advanced packaging demand forecasts
  • Server tray and rack architecture forecasts
  • Cost of AI inference and training, and the token cost roadmap
  • Demand drivers: agentic AI, physical AI, recommendation, coding, search
  • Capital expenditure versus operating expenditure economics
  • Ecosystem, supply chain and co-designer relationship maps
  • Market share by revenue, units and deployment volume
  • Financial analysis of the leading chip designers
  • AI semiconductor start-up funding landscape
  • Mainland China market, manufacturers and supply chain
  • CPU, GPU and AI ASIC technology and roadmap analysis
  • HBM, advanced packaging and rack bill of materials
  • Emerging architectures: photonics, neuromorphic, quantum
  • Bull, base and bear scenarios to 2040, risks and investment outlook
  • 81 company profiles. Companies profiled include Advanced Micro Devices (AMD), Alchip Technologies, Alibaba (T-Head Semiconductor), Alphawave Semi, Amazon Web Services (Annapurna Labs), Amkor Technology, Ampere Computing, Analog Devices, Applied Materials, Arm Holdings, ASE Technology Holding, ASML Holding, Astera Labs, Axelera AI, Baidu (Kunlun), Biren Technology, Broadcom, Cadence Design Systems, Cambricon Technologies, Celestica, Cerebras Systems, Cisco Systems, Coherent Corp, CoreWeave, Credo Technology Group, ChangXin Memory Technologies (CXMT), d-Matrix, Delta Electronics, Dell Technologies, Eaton Corporation, Enflame Technology, Etched, Foxconn (Hon Hai Precision Industry), FuriosaAI, GlobalFoundries, Google (Alphabet), Groq, Global Unichip Corporation (GUC), Hewlett Packard Enterprise, Hygon Information Technology, Ibiden, Iluvatar CoreX, Infineon Technologies, Innolight Technology, Intel Corporation, JCET Group, KLA Corporation, Lam Research, Lightmatter, Lumentum Holdings, Marvell Technology, MediaTek, Meta Platforms, Micron Technology, Microsoft, Monolithic Power Systems, Moore Threads, Nebius Group, NVIDIA Corporation, onsemi, OpenAI, Powertech Technology, Quanta Computer, Rambus, Rebellions, Renesas Electronics, Samsung Electronics, SambaNova Systems, Schneider Electric, Shinko Electric Industries, SiPearl, SK Hynix, Semiconductor Manufacturing International Corporation (SMIC), Super Micro Computer, Synopsys, Tenstorrent, Tokyo Electron, Taiwan Semiconductor Manufacturing Company (TSMC), Unimicron Technology, Vertiv Holdings, and Wistron Corporation.

Table of Contents

1 EXECUTIVE SUMMARY

  • 1.1 Global AI Infrastructure and Investment Landscape
  • 1.2 Capital formation and the venture channel
  • 1.3 US and Chinese Hyperscaler CapEx Trends and Projections
  • 1.4 Power as the binding constraint
  • 1.5 AI Data Center Typology and Demand Segmentation
  • 1.6 AI Regulatory Landscape and Export Controls
  • 1.7 Sovereign AI and International Industrial Policy
  • 1.8 The US–China Technology Divide

2 MARKET FORECASTS

  • 2.1 Processor Revenue Forecast
    • 2.1.1 Total Data Center Processor Market, 2021–2040
    • 2.1.2 GPU Revenue Forecast, 2021–2040 (p. 64)
    • 2.1.3 AI ASIC Revenue Forecast, 2021–2040 (p. 68)
    • 2.1.4 Server CPU Revenue Forecast, 2021–2040 (p. 71)
    • 2.1.5 FPGA Data Center Revenue Forecast, 2021–2040
  • 2.2 Average Selling Price (ASP) Forecast
    • 2.2.1 GPU ASP Trends by Product Tier, 2021–2040
    • 2.2.2 AI ASIC ASP Trends by Hyperscaler, 2021–2040
    • 2.2.3 CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 (p. 84)
  • 2.3 Processor Volume Forecast
    • 2.3.1 GPU Unit Shipments by Vendor, 2021–2040
    • 2.3.2 AI ASIC Unit Shipments by Hyperscaler, 2021–2040
    • 2.3.3 CPU Unit Shipments by Vendor, 2021–2040
  • 2.4 Wafer Forecast
    • 2.4.1 GPU & AI ASIC Wafer Starts by Technology Node, 2021–2040
    • 2.4.2 Wafer Starts by Foundry
    • 2.4.3 GPU & AI ASIC Compute Die Forecast, 2021–2040
    • 2.4.4 HBM-Driven Revenue Separation from GPU & AI ASIC
  • 2.5 Server Tray Volume Forecast
  • 2.6 AI server rack architecture
  • 2.7 CPU Focus
  • 2.8 The Arm ramp
  • 2.9 GPU & AI ASIC Focus

3 MARKET TRENDS

  • 3.1 Cost of Generative AI Inference and Training
  • 3.2 Why training costs what it does
  • 3.3 From Agentic AI to Physical AI
  • 3.4 Physical AI
  • 3.5 Recommendation Models for Social Networks
  • 3.6 Coding Assistants
  • 3.7 Search Engine vs. LLM
  • 3.8 OpenClaw
    • 3.8.1 What it is
    • 3.8.2 Why it matters to this forecast
  • 3.9 CapEx vs. OpEx in the Era of Generative AI
  • 3.10 Is the Future of AI Data Centers in Space?

4 MARKET SHARE & SUPPLY CHAIN

  • 4.1 Data Center Ecosystem Map
  • 4.2 Foundation Models Ecosystem Map
  • 4.3 U.S. vs. China Tech War — Timeline
  • 4.4 Financial Metrics of Data Center Chip Designers
  • 4.5 AI semiconductor start-up fundraising
  • 4.6 Case Study: OpenAI Revenue and Gigawatt
  • 4.7 Market Share: CPU, GPU, AI ASIC & XPU Co-Designers
    • 4.7.1 GPU Market Share by Revenue and Units (p. 247)
    • 4.7.2 AI ASIC Market Share by Hyperscaler
    • 4.7.3 CPU Market Share by Vendor
    • 4.7.4 XPU Co-Designer Revenue Market Share
  • 4.8 Focus on China

5 TECHNOLOGY ANALYSIS

  • 5.1 CPU Technology Trends
    • 5.1.1 x86 Architecture Evolution
    • 5.1.2 Arm-Based CPU Momentum in the Data Center
    • 5.1.3 RISC-V in the Data Center (p. 289)
    • 5.1.4 CPU Specialization for AI Workloads
  • 5.2 GPU Technology Trends
    • 5.2.1 Chiplet and Multi-Die Architectures
    • 5.2.2 Rack-Scale GPU Architectures — NVL72 and Beyond
    • 5.2.3 Memory Bandwidth and HBM Integration
    • 5.2.4 Networking and Interconnect Evolution
  • 5.3 AI Data Center Traffic Patterns and Network Architecture
    • 5.3.1 Four tiers, four different problems
    • 5.3.2 Why the traffic looks the way it does
  • 5.4 AI ASIC Technology Trends
    • 5.4.1 Hyperscaler ASIC Product Roadmaps
    • 5.4.2 AI ASIC Start-Up Landscape
    • 5.4.3 AI ASIC Technology Specification Database
    • 5.4.4 Compute Disaggregation for AI Inference
    • 5.4.5 KV Cache and the Storage Tier
  • 5.5 GPU vs. AI ASIC: Comparative Analysis
  • 5.6 Advanced Packaging and HBM Memory
    • 5.6.1 HBM Technology Roadmap — HBM2E to HBM4
    • 5.6.2 CoWoS and Advanced Packaging Capacity
    • 5.6.3 Custom HBM and Co-Design Trends
    • 5.6.4 AI Rack Bill of Materials
    • 5.6.5 Thermal Management and Cooling Technologies
  • 5.7 Emerging Computing Architectures
    • 5.7.1 Photonic Computing
    • 5.7.2 Neuromorphic Computing
    • 5.7.3 Quantum Computing Outlook

6 OUTLOOK & SCENARIOS

  • 6.1 Market Outlook 2026–2040
  • 6.2 Technology Outlook 2026–2040
  • 6.3 Key Risks and Opportunities
  • 6.4 Strategic Recommendations

7 COMPANY PROFILES (81 company profiles)

8 REPORT METHODOLOGY

  • 8.1 Objective of the report
  • 8.2 Scope of this report

9 GLOSSARY OF TERMS AND ABBREVIATIONS

10 REFERENCES

List of Tables

  • Table 1. Global AI Infrastructure Investment by Category, 2021–2040 ($B)
  • Table 2. US Early-Stage Venture Deployment in AI and Adjacent Technologies, 2016–2026 YTD
  • Table 3. Early-stage round size and investor concentration, 2026 YTD
  • Table 4. US vs. Chinese Hyperscaler CapEx, 2021–2040 ($B)
  • Table 5. Data Center Installed IT Load by Workload Type, 2024–2040 (GW)
  • Table 6. AI Data Center Facility Types: Characteristics and Processor Preferences
  • Table 7. Data Center Processor Demand by Facility Type, 2021–2040 ($B)
  • Table 8. Sovereign AI and Semiconductor Programs
  • Table 9. Sovereign and Government AI Processor Demand by Region, 2025–2040 ($B)
  • Table 10. Data Center Processor Market Revenue Summary, 2021–2040 ($B)
  • Table 11. Revenue Breakdown by Processor Type (CPU, GPU, AI ASIC, FPGA), 2021–2040
  • Table 12. GPU Revenue by Vendor, 2021–2040 ($B)
  • Table 13. Nvidia GPU Revenue by Product Generation, 2021–2028 ($B)
  • Table 14. AMD GPU Revenue by Product Generation, 2021–2028 ($B)
  • Table 15. AI ASIC Revenue by Hyperscaler, 2021–2040 ($B)
  • Table 16. Server CPU Revenue by Vendor, 2021–2040 ($B)
  • Table 17. FPGA Data Center Revenue Forecast, 2021–2040 ($M)
  • Table 18. GPU ASP by Product Tier, 2021–2040 ($K per unit)
  • Table 19. AI ASIC ASP by Hyperscaler, 2021–2040 ($K per unit)
  • Table 20. Server CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 ($)
  • Table 21. GPU Unit Shipments by Vendor, 2021–2040 (K units)
  • Table 22. Nvidia GPU Unit Shipments by Product Generation, 2021–2028 (K units)
  • Table 23. AMD GPU Unit Shipments by Product Generation, 2021–2028 (K units)
  • Table 24. AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units)
  • Table 25. CPU Unit Shipments by Vendor, 2021–2040 (M units)
  • Table 26. Hyperscaler Custom CPU Unit Adoption, 2022–2040 (M units)
  • Table 27. GPU & AI ASIC Wafer Starts by Node and Foundry, 2021–2040 (KWPM, 300mm equivalent)
  • Table 28. Average Die Size Trend — GPU vs. AI ASIC, 2021–2040 (mm²)
  • Table 29. HBM Revenue Separated from GPU & AI ASIC Total, 2021–2040 ($B)
  • Table 30. AI Server vs. General-Purpose Server Tray Volume, 2021–2040 (M units)
  • Table 31. AI Server Rack Configuration and Architecture, 2025–2040
  • Table 32. CPU Market Share by Revenue: Intel vs. AMD vs. Arm-based, 2021–2040
  • Table 33. Hyperscaler Arm CPU Deployment Ramp, 2022–2040
  • Table 34. CPU Processor Roadmap Summary — Major Vendors, 2024–2030
  • Table 35.GPU Market Share by Revenue, 2021–2040 (%)
  • Table 36. AI ASIC Market Share by Deployment Volume, 2021–2040 (%)
  • Table 37. GPU & AI ASIC Split by Technology Node, 2021–2040
  • Table 38. GPU & AI ASIC Product Roadmap Summary, 2024–2030
  • Table 39. Cost per Token Trend: Training and Inference, 2021–2040 ($/M tokens)
  • Table 40. Cost per Token by Model Size and Hardware Configuration, 2024–2040 ($/M output tokens)
  • Table 41. Cost per Token Trend: Training and Inference, 2021–2040 ($/M tokens)
  • Table 42. Inference Cost Breakdown by Infrastructure Component, 2025 (%)
  • Table 43. AI Model Parameter Count vs. Hardware Requirements, 2020–2028
  • Table 44. Agentic AI Use Cases by Industry and Hardware Requirements
  • Table 45. AI Agent Deployment Forecast by Sector, 2025–2040 (M concurrently deployed agents)
  • Table 46. Physical AI Hardware Requirements vs. Generative AI, 2025–2040
  • Table 47. Robotics Semiconductor Market Forecast, 2024–2040 ($B)
  • Table 48. Recommendation Model Architecture Evolution, 2018–2028
  • Table 49. Recommendation Model Compute Demand by Platform, 2024–2040 ($B)
  • Table 50. AI-Powered Coding Assistant Market Share, 2024–2028 (%)
  • Table 51. Coding Assistant Market Share and Underlying Infrastructure
  • Table 52. Coding AI GPU Compute Demand, 2024–2040 ($B)
  • Table 53. LLM vs. Traditional Search: Query Volume Forecast, 2022–2040 (B queries/day)
  • Table 54. AI Search Compute Infrastructure Requirements, 2024–2040
  • Table 55. CapEx Cycle: US Hyperscalers, 2015–2040 ($B)
  • Table 56. CapEx-to-Revenue Ratio: Major Hyperscalers, 2020–2040 (%)
  • Table 57. AI Infrastructure OpEx vs. CapEx Split, 2024–2040
  • Table 58. Cloud AI Chip Rental vs. Ownership Economics, 2025–2040
  • Table 59. Low Earth Orbit Latency and Bandwidth Projections, 2025–2035
  • Table 60. AI Chip Supply Chain: From Silicon to Hyperscaler
  • Table 61. Foundation Model Training Infrastructure by Developer
  • Table 62. Chinese AI Chip Import Replacement Progress, 2022–2028 (%)
  • Table 63. Sanctioned vs. Unsanctioned Chinese AI Chip Revenues, 2022–2028 ($B)
  • Table 64. Financial Metrics: Top 10 Data Center Chip Designers, 2021–2025
  • Table 65. Gross Margin Comparison: Nvidia vs. AMD vs. Intel, 2020–2025 (%)
  • Table 66. R&D Spend as % of Revenue: Key Chip Designers, 2020–2025
  • Table 67. US and Chinese Hyperscaler CapEx Summary, 2021–2026 ($B)
  • Table 68. AI Semiconductor Start-Up Fundraising, 2019–Q1 2026 ($M)
  • Table 69. AI Semiconductor Start-Up Fundraising Database, 2019–Q1 2026
  • Table 70. OpenAI Revenue Forecast, 2023–2030 ($B)
  • Table 71. OpenAI Compute Demand (Gigawatt), 2023–2030
  • Table 72. OpenAI GPU Procurement Forecast by Generation, 2023–2028 (K units)
  • Table 73. GPU Market Share Summary by Revenue and Units, 2021–2025
  • Table 74. Nvidia, AMD, Google, AWS GPU/ASIC Unit Split, 2021–2028 (K units)
  • Table 75. AI ASIC Market Share by Hyperscaler, 2021–2025 (%)
  • Table 76. AI ASIC Specifications: Google, AWS, Microsoft, Meta, 2024–2026
  • Table 77. CPU Market Share by Revenue: Intel vs. AMD vs. Arm, 2021–2025 (%)
  • Table 78. Hyperscaler Custom CPU Market Share Evolution, 2022–2028
  • Table 79. Co-Designer Revenue Share, 2021–2026 (%)
  • Table 80. China Data Center Processor Market by Type, 2021–2030 ($B)
  • Table 81. Chinese Hyperscaler Processor Demand, 2021–2028 ($B)
  • Table 82. Domestic Chinese AI Chip Makers: Unit Share, 2022–2028 (%)
  • Table 83. Chinese AI Chip Makers: Product Specifications and Capabilities
  • Table 84. China Data Center Semiconductor Supply Chain Map
  • Table 85. CPU Architecture Comparison: x86, Arm, RISC-V for the Data Center
  • Table 86. CPU Specifications: Intel, AMD, AWS, Google, Microsoft, Huawei, Nvidia, 2024–2026
  • Table 87. Arm Server CPU Shipment Forecast, 2022–2040 (M units)
  • Table 88. RISC-V Data Center Adoption Forecast, 2025–2040
  • Table 89. CPU Specialization for AI Inference Workloads
  • Table 90. GPU Specifications: Nvidia Blackwell, Rubin; AMD MI350X, MI450, 2024–2026
  • Table 91. GPU Die Size Evolution and Chiplet Transition, 2020–2030 (mm²)
  • Table 92. Rack-Scale GPU Architecture: NVL72 and Next-Generation Platforms
  • Table 93. GPU Memory Bandwidth Trend: HBM Generations, 2020–2030 (TB/s)
  • Table 94. NVLink and Interconnect Bandwidth Evolution, 2020–2030
  • Table 95. AI Network Tiers: Bandwidth, Latency and Technology
  • Table 96. Parallelism Strategies and Their Network Traffic Characteristics
  • Table 97. AI ASIC Technology Specification Database
  • Table 98. Memory and Storage Tiers for KV Cache in Inference Serving
  • Table 99. GPU vs. AI ASIC: Performance per Watt Comparison, 2022–2026
  • Table 100. GPU vs. AI ASIC: Training vs. Inference Suitability Matrix
  • Table 101. GPU vs. AI ASIC: Total Cost of Ownership Analysis
  • Table 102. HBM Specification Comparison: HBM2E, HBM3, HBM3E, HBM4
  • Table 103. CoWoS Capacity Expansion Roadmap: TSMC, 2022–2028 (KWPM)
  • Table 104. Advanced Packaging Market Share: CoWoS, SoIC, Others, 2024–2028 (%)
  • Table 105. AI Server Rack BoM: Itemized Cost Breakdown, 2025 ($K)
  • Table 106. AI Rack BoM Cost Evolution, 2023–2028 ($K)
  • Table 107. Data Center Cooling Technologies for AI: Comparison
  • Table 108. Cooling Technology Share of AI Accelerator Deployments, 2023–2040 (%)
  • Table 109. Silicon Photonics Market Forecast in Data Centers, 2024–2040 ($B)
  • Table 110. Neuromorphic Computing Roadmap, 2024–2040
  • Table 111. Quantum Computing Timeline to Commercial Viability, 2025–2040
  • Table 112. Emerging Computing Technology Readiness Assessment
  • Table 113. Market Forecast Summary: Bull / Base / Bear Scenarios, 2026–2040 ($B)
  • Table 114. Bull, Base, Bear Case Revenue Scenarios by Processor Type, 2040
  • Table 115. Technology Roadmap Summary: CPU, GPU, AI ASIC, 2026–2040
  • Table 116. Key Risk Register: Probability and Impact Assessment
  • Table 117. Investment Opportunity Map: Data Center Semiconductor Ecosystem

List of Figures

  • Figure 1. Global AI Infrastructure Investment Forecast, 2021–2040 ($B)
  • Figure 2. US vs. Chinese Hyperscaler CapEx, 2021–2040 ($B)
  • Figure 3. Data Center Power Consumption Forecast, 2024–2040 (GW installed IT load)
  • Figure 4. AI-Related Data Center Construction Starts by Region, 2022–2028 (GW of IT capacity)
  • Figure 5. Data Center Processor Demand by Facility Type, 2021–2040 ($B)
  • Figure 6. US Export Controls on AI Chips: Key Milestones, 2019–2026
  • Figure 7. Sovereign and Government AI Processor Demand by Region, 2025–2040 ($B)
  • Figure 8. US–China Technology Decoupling Timeline, 2018–2026
  • Figure 9. Total Data Center Processor Market Revenue Forecast, 2021–2040 ($B)
  • Figure 10. Revenue Breakdown by Processor Type (CPU, GPU, AI ASIC, FPGA), 2021–2040
  • Figure 11. Data Center Processor CAGR by Category, 2025–2040 (%)
  • Figure 12. GPU Market Revenue Forecast, 2021–2040 ($B)
  • Figure 13. GPU Revenue Split by Vendor (Nvidia, AMD, Others), 2021–2040
  • Figure 14. Nvidia GPU Revenue by Product Generation, 2021–2028 ($B)
  • Figure 15. AMD GPU Revenue by Product Generation, 2021–2028 ($B)
  • Figure 16. AI ASIC Market Revenue Forecast, 2021–2040 ($B)
  • Figure 17. AI ASIC Revenue Split by Hyperscaler, 2021–2040
  • Figure 18. Server CPU Market Revenue Forecast, 2021–2040 ($B)
  • Figure 19. Server CPU Revenue Split by Architecture (x86 vs. Arm), 2021–2040
  • Figure 20. FPGA Data Center Revenue Forecast, 2021–2040 ($M)
  • Figure 21. GPU ASP Evolution by Product Tier, 2021–2040 ($K)
  • Figure 22. AI ASIC ASP Trends by Hyperscaler, 2021–2040 ($K)
  • Figure 23. Server CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 ($)
  • Figure 24. GPU Unit Shipments by Vendor, 2021–2040 (K units)
  • Figure 25. Nvidia GPU Unit Shipments by Product Generation, 2021–2028 (K units)
  • Figure 26. AMD GPU Unit Shipments by Product Generation, 2021–2028 (K units)
  • Figure 27. AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units)
  • Figure 28. Google TPU Unit Deployment Forecast, 2021–2040 (K units)
  • Figure 29. AWS Trainium & Inferentia Unit Forecast, 2021–2040 (K units)
  • Figure 30. Microsoft MAIA Unit Forecast, 2021–2040 (K units)
  • Figure 31. CPU Unit Shipments — Data Center, 2021–2040 (M units)
  • Figure 32. Intel vs. AMD CPU Market Share in Unit Terms, 2021–2040 (%)
  • Figure 33. Hyperscaler Custom CPU Unit Adoption, 2022–2040 (M units)
  • Figure 34. GPU & AI ASIC Wafer Starts by Technology Node, 2021–2040 (KWPM)
  • Figure 35. Wafer Consumption Split: Advanced Nodes, 2021–2040
  • Figure 36. GPU & AI ASIC Wafer Starts by Foundry, 2021–2040
  • Figure 37. TSMC Advanced Node Capacity Forecast, 2024–2040 (KWPM)
  • Figure 38. GPU & AI ASIC Compute Die Forecast, 2021–2040
  • Figure 39. Average Die Size Trend — GPU vs. AI ASIC, 2021–2040 (mm²)
  • Figure 40. HBM Revenue Separated from GPU & AI ASIC Total, 2021–2040 ($B)
  • Figure 41. AI Server vs. General-Purpose Server Tray Volume, 2021–2040 (M units)
  • Figure 42. AI Server Rack Configuration and Architecture, 2025–2040
  • Figure 43. CPU Market Share by Revenue: Intel vs. AMD vs. Arm-based, 2021–2040
  • Figure 44. Hyperscaler Arm CPU Deployment Ramp, 2022–2040
  • Figure 45. CPU Product Roadmap: Intel, AMD, Arm, Google, AWS, Nvidia, 2024–2030
  • Figure 46. GPU Market Share by Revenue, 2021–2040 (%)
  • Figure 47. AI ASIC Market Share by Deployment Volume, 2021–2040 (%)
  • Figure 48. GPU & AI ASIC Split by Technology Node, 2021–2040
  • Figure 49. Cost per Token Trend: Training and Inference, 2021–2040 ($/M tokens)
  • Figure 50. Training Compute Requirements by Model Type, 2020–2028 (FLOPs)
  • Figure 51. Inference Cost Breakdown by Infrastructure Component, 2025 (%)
  • Figure 52. Token Cost Reduction Roadmap, 2025–2040
  • Figure 53. AI Model Parameter Count vs. Hardware Requirements, 2020–2028
  • Figure 54. Agentic AI Market Taxonomy and Use Cases
  • Figure 55. AI Agent Deployment Forecast by Sector, 2025–2040 (M concurrently deployed agents)
  • Figure 56. Physical AI Hardware Requirements vs. Generative AI, 2025–2040
  • Figure 57. Robotics Semiconductor Market Forecast, 2024–2040 ($B)
  • Figure 58. Recommendation Model Architecture Evolution, 2018–2028
  • Figure 59. Recommendation Model Compute Demand by Platform, 2024–2040 ($B)
  • Figure 60. AI-Powered Coding Assistant Market Share, 2024–2028 (%)
  • Figure 61. Coding AI GPU Compute Demand, 2024–2040 ($B)
  • Figure 62. LLM vs. Traditional Search: Query Volume Forecast, 2022–2040 (B queries/day)
  • Figure 63. AI Search Compute Infrastructure Requirements, 2024–2040
  • Figure 64. CapEx Cycle: US Hyperscalers, 2015–2040 ($B)
  • Figure 65. CapEx-to-Revenue Ratio: Major Hyperscalers, 2020–2040 (%)
  • Figure 66. AI Infrastructure OpEx vs. CapEx Split, 2024–2040
  • Figure 67. Cloud AI Chip Rental vs. Ownership Economics, 2025–2040
  • Figure 68. Space-Based Data Center Conceptual Architecture
  • Figure 69. Low Earth Orbit Latency and Bandwidth Projections, 2025–2035
  • Figure 70. Global Data Center Processor Ecosystem Map
  • Figure 71. AI Chip Supply Chain: From Silicon to Hyperscaler Source: Future Markets, Inc., 2026
  • Figure 72. Co-Designer and Hyperscaler Relationship Map
  • Figure 73. OSAT and Advanced Packaging Supply Chain Map
  • Figure 74. Foundation Models Ecosystem Map: Developers and Infrastructure
  • Figure 75. Open vs. Closed Source AI Model Landscape, 2024
  • Figure 76. Foundation Model Training Infrastructure by Developer
  • Figure 77. US Export Control Timeline: Semiconductors, 2018–2026
  • Figure 78. Chinese AI Chip Import Replacement Progress, 2022–2028 (%)
  • Figure 79. Sanctioned vs. Unsanctioned Chinese AI Chip Revenues, 2022–2028 ($B)
  • Figure 80. Comparative Revenue: Data Center Chip Designers, 2021–2025 ($B)
  • Figure 81. Gross Margin Comparison: Nvidia vs. AMD vs. Intel, 2020–2025 (%)
  • Figure 82. R&D Spend as % of Revenue: Key Chip Designers, 2020–2025
  • Figure 83. AI Semiconductor Start-Up Fundraising, 2019–Q1 2026 ($M)
  • Figure 84. OpenAI Revenue Forecast, 2023–2030 ($B)
  • Figure 85. OpenAI Compute Demand (Gigawatt), 2023–2030
  • Figure 86. OpenAI GPU Procurement Forecast by Generation, 2023–2028 (K units)
  • Figure 87. GPU Market Share by Revenue, 2021–2025 (%)
  • Figure 88. GPU Market Share by Units, 2021–2025 (%)
  • Figure 89. Nvidia, AMD, Google, AWS GPU/ASIC Unit Split, 2021–2028 (K units)
  • Figure 90. AI ASIC Market Share by Hyperscaler, 2021–2025 (%)
  • Figure 91. CPU Market Share by Revenue: Intel vs. AMD vs. Arm, 2021–2025 (%)
  • Figure 92. Hyperscaler Custom CPU Market Share Evolution, 2022–2028
  • Figure 93. XPU Co-Designer Revenue, 2023–2026 ($B)
  • Figure 94. XPU Co-Designer Revenue Share, 2021–2026 (%)
  • Figure 95. China Data Center Processor Market Size, 2021–2025 ($B)
  • Figure 96. Chinese Hyperscaler Processor Demand, 2021–2028 ($B)
  • Figure 97. Domestic Chinese AI Chip Makers: Unit Share, 2022–2028 (%)
  • Figure 98. HiSilicon, Cambricon, Baidu Kunlun, Hygon Roadmap, 2024–2028
  • Figure 99. China Data Center Semiconductor Supply Chain Map
  • Figure 100. CPU Architecture Comparison: x86, Arm, RISC-V for the Data Center
  • Figure 101. Arm Server CPU Shipment Forecast, 2022–2040 (M units)
  • Figure 102. RISC-V Data Center Adoption Forecast, 2025–2040
  • Figure 103. CPU Specialization for AI Inference Workloads
  • Figure 104. GPU Process Node Roadmap: Nvidia, AMD, 2020–2030
  • Figure 105. GPU Die Size Evolution and Chiplet Transition, 2020–2030 (mm²)
  • Figure 106. Rack-Scale GPU Architecture: NVL72 and Next-Generation Platforms
  • Figure 107. GPU Memory Bandwidth Trend: HBM Generations, 2020–2030 (TB/s)
  • Figure 108. NVLink and Interconnect Bandwidth Evolution, 2020–2030
  • Figure 109. AI Data Center Network Architecture by Tier
  • Figure 110. Hyperscaler ASIC Roadmap Comparison: Google, AWS, Microsoft, Meta
  • Figure 111. AI ASIC Start-Up Landscape by Funding Stage, 2024
  • Figure 112. AI ASIC Technology Specification Matrix
  • Figure 113. Disaggregated Inference Architecture Diagram
  • Figure 114. GPU vs. AI ASIC: Performance per Watt Comparison, 2022–2026
  • Figure 115. GPU vs. AI ASIC: Training vs. Inference Suitability Matrix
  • Figure 116. GPU vs. AI ASIC: Total Cost of Ownership Analysis
  • Figure 117. HBM Technology Roadmap: HBM2E to HBM4, 2020–2028
  • Figure 118. HBM Bandwidth and Capacity per Stack by Generation, 2020–2028
  • Figure 119. CoWoS Capacity Expansion Roadmap: TSMC, 2022–2028 (KWPM)
  • Figure 120. Advanced Packaging Market Share: CoWoS, SoIC, Others, 2024–2028 (%)
  • Figure 121. Custom HBM Co-Design Relationships Map
  • Figure 122. AI Server Rack Bill of Materials: Component Breakdown, 2025 ($K)
  • Figure 123. AI Rack BoM Cost Evolution, 2023–2028 ($K)
  • Figure 124. Rack Density Envelope by Cooling Technology
  • Figure 125. Cooling Technology Share of AI Accelerator Deployments, 2023–2040 (%)
  • Figure 126. Silicon Photonics Market Forecast in Data Centers, 2024–2040 ($B)
  • Figure 127. Neuromorphic Computing Roadmap, 2024–2040
  • Figure 128. Quantum Computing Timeline to Commercial Viability, 2025–2040
  • Figure 129. Data Center Processor Market Scenario Analysis, 2026–2040 ($B)
  • Figure 130. Bull, Base, Bear Case Revenue Scenarios by Processor Type, 2040
  • Figure 131. Technology Roadmap Summary: CPU, GPU, AI ASIC, 2026–2040
  • Figure 132. Competitive Landscape Risk Matrix, 2026–2040
  • Figure 133. Investment Opportunity Map: Data Center Semiconductor Ecosystem