The Global Market
完整報告名稱與涵蓋範圍
The Global Market for Computing and AI for Data Centers 2026–2040
報告摘要
The global market for computing and artificial intelligence in data centers represents one of the most dynamic and capital-intensive segments of the semiconductor industry. Driven by the rapid proliferation of generative AI, large language models, and agentic AI systems, demand for specialised data center processors — encompassing GPUs, AI ASICs, CPUs, and FPGAs — has entered a period of extraordinary and sustained growth. From a market valued at approximately $215 billion in 2025, the sector is projected to scale dramatically through 2040, as hyperscalers, cloud providers, and enterprises race to build the compute infrastructure required to train, fine-tune, and serve increasingly powerful AI models.
At the core of this expansion is the GPU, which remains the dominant processor architecture for AI workloads due to its unmatched parallel processing capability and mature software ecosystem. Nvidia continues to hold an overwhelming share of this segment, with successive generations — from Hopper to Blackwell to Rubin and beyond — each delivering step-change improvements in compute density, memory bandwidth, and energy efficiency. AMD provides meaningful competition with its MI-series accelerators, while the broader landscape is being reshaped by hyperscalers developing their own custom silicon to reduce dependency on merchant chip vendors and lower total cost of ownership.
AI ASICs represent the fastest-growing processor category, as companies including Google, Amazon Web Services, Microsoft, and Meta invest heavily in purpose-built chips optimised for specific workloads such as inference, recommendation, and training. These internally developed accelerators — including Google's TPU series, AWS Trainium and Inferentia, Microsoft MAIA, and Meta's MTIA — are increasingly displacing third-party GPUs for certain use cases, fundamentally altering the competitive dynamics of the market and creating a parallel ecosystem of chip co-designers and advanced packaging specialists.
The server CPU market, though more mature, continues to evolve rapidly. Intel and AMD maintain leading positions with their x86 architectures, but face mounting pressure from Arm-based alternatives championed by hyperscalers such as AWS with Graviton, Google with Axion, Microsoft with Cobalt, and Nvidia with Grace and Vera. RISC-V is also emerging as a credible contender for specific workloads, particularly as open-source hardware ecosystems mature. Meanwhile, FPGAs continue to serve niche roles in low-latency and specialised inference applications.
Underpinning all of this is a complex and increasingly strained supply chain. Advanced semiconductor manufacturing is concentrated at TSMC, Samsung, and Intel Foundry, with leading-edge nodes below 5nm commanding the majority of AI chip demand. High Bandwidth Memory, supplied primarily by SK Hynix, Samsung, and Micron, has emerged as a critical bottleneck, while advanced packaging technologies such as CoWoS are operating at near-full capacity. Hyperscaler capital expenditure continues to flow into data centre construction, power infrastructure, and silicon procurement at a scale that is reshaping global semiconductor supply chains.
Geopolitics adds a further layer of complexity. US export controls on advanced AI chips have accelerated China's drive toward semiconductor self-sufficiency, with domestic players such as Huawei HiSilicon, Cambricon, Biren, and Hygon developing increasingly capable alternatives. The bifurcation of the global AI compute market into US-aligned and China-domestic supply chains is one of the defining structural trends of the decade, with profound implications for technology strategy, investment allocation, and national industrial policy.
The Global Market for Computing and AI for Data Centers 2026–2040 is a comprehensive strategic intelligence report covering the full landscape of data centre processor technology, market dynamics, competitive positioning, and long-range forecasting through to 2040. Produced for technology executives, semiconductor investors, strategic planners, and policy analysts, the report provides the depth of quantitative rigour and qualitative insight required to navigate one of the most rapidly evolving markets in the global economy.
The report opens with a set of preliminary materials including a detailed glossary of technical terms and abbreviations, a clear articulation of research objectives and scope, biographical profiles of the authoring team, and a candid retrospective on previous forecast accuracy. This is followed by a three-page summary and a full executive summary designed for senior readers who require rapid orientation to the report's key findings without sacrificing analytical depth.
Chapter one establishes the macroeconomic and geopolitical context, examining global AI infrastructure investment trends, hyperscaler capital expenditure trajectories for both US and Chinese players, the evolving regulatory landscape including US export controls, and the widening technology divide between Western and Chinese semiconductor ecosystems.
Chapter two forms the quantitative heart of the report, delivering granular market forecasts from 2021 to 2040 across all major processor categories. Revenue, average selling price, unit volume, wafer consumption, and server tray forecasts are provided at the vendor, product, and technology node level, enabling readers to build detailed bottom-up views of market opportunity and competitive exposure. Separate analytical lenses are provided for CPU, GPU, and AI ASIC dynamics, including HBM-driven revenue disaggregation and compute die forecasting.
Chapter three addresses the market forces shaping demand, including the falling cost of generative AI inference and training, the emergence of agentic and physical AI, the compute demands of recommendation engines and coding assistants, the competition between LLMs and traditional search, and broader questions around the CapEx and OpEx economics of AI infrastructure. An exploratory section examines the longer-term possibility of space-based data center architectures.
Chapter four maps the competitive landscape in detail, providing ecosystem maps for both the data center processor supply chain and the foundation model developer community. It includes financial benchmarking of leading chip designers, a deep-dive case study on OpenAI's revenue and compute trajectory, comprehensive market share analysis, and a dedicated section on Mainland China covering domestic market sizing, hyperscaler demand, manufacturer profiles, and supply chain structure.
Chapter five delivers an authoritative review of technology trends across all processor categories, covering process node roadmaps, chiplet architectures, rack-scale system designs, memory and packaging technology, and emerging computing paradigms including photonics, neuromorphic, and quantum computing. Unique assets include a full AI ASIC technology specification database and a start-up landscape analysis.
The report concludes with a forward-looking outlook chapter presenting bull, base, and bear case scenarios for the market through 2031 and beyond to 2040, a comprehensive risk register, and strategic recommendations. An extensive company profiles section — covering 81 organisations with one dedicated page per company — rounds out the report, providing standardised strategic and financial snapshots of every major player in the ecosystem.
Report Contents include:
Global AI infrastructure and investment landscape
US and Chinese hyperscaler CapEx trends and projections
AI regulatory landscape and export controls
The US–China technology divide
Market Forecasts (2021–2040)
Total data centre processor revenue forecast
GPU, AI ASIC, CPU and FPGA revenue forecasts
Average selling price (ASP) forecasts by vendor and product tier
Processor unit shipment forecasts
Wafer starts by technology node and foundry (TSMC, Samsung, Intel Foundry)
GPU and AI ASIC compute die forecasts
HBM-driven revenue separation
Server tray volume forecasts
Dedicated CPU focus and GPU/AI ASIC focus sections
Market Trends
Cost of generative AI inference and training
From agentic AI to physical AI
Recommendation models for social networks
Coding assistants
Search engines vs. LLMs
OpenClaw
CapEx vs. OpEx in the generative AI era
The future of space-based AI data centres
Market Share & Supply Chain
Data center ecosystem map
Foundation models ecosystem map
US vs. China tech war timeline
Financial metrics of data center chip designers
Case study: OpenAI revenue and gigawatt forecast
Market share analysis — CPU, GPU, AI ASIC, XPU co-designers
Mainland China focus: market size, hyperscaler demand, manufacturer profiles, supply chain
Technology Trends
CPU: x86, Arm, RISC-V, workload specialisation
GPU: process nodes, chiplets, rack-scale architecture, HBM integration, interconnects
AI ASIC: hyperscaler roadmaps, start-up landscape, specification database, disaggregated inference
GPU vs. AI ASIC comparative analysis
Advanced packaging and HBM (HBM2E through HBM4), CoWoS, AI rack bill of materials
Emerging computing: photonics, neuromorphic, quantum
Outlook
Market outlook 2026–2040 with bull/base/bear scenarios
Technology outlook 2026–2040
Key risks and opportunities
Strategic recommendations
Company Profiles
81 individual company profiles, one page per company, covering strategy, products, financials, and roadmap. Companies profiled include 01.AI, Achronix Semiconductor, Advanced Micro Devices (AMD), AI21 Labs, Alchip Technologies, Aleph Alpha, Alibaba Group / T-Head Semiconductor, Amazon Web Services (AWS), Ampere Computing, Anthropic, Arm Holdings, Axelera AI, Baidu, Biren Technology, Broadcom, ByteDance, Cambricon Technologies, Cerebras Systems, China Mobile, Cisco Systems, Cohere, CoreWeave, d-Matrix, DeepSeek, Dell Technologies, Enflame Technology, Esperanto Technologies, Etched, Fujitsu, Furiosa AI, GlobalFoundries (GF), Google (DeepMind / TPU Programme), GrAI Matter Labs, Graphcore, Groq, GUC (Global Unichip Corp.), Hewlett Packard Enterprise (HPE), HiSilicon Technologies, Huawei Technologies, Hygon Information Technology, IBM, Iluvatar CoreX, Intel Corporation, Kalray, Lattice Semiconductor, Lightmatter and more
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目錄 Table of Contents
PRELIMINARY SECTIONS
Glossary of Terms and Abbreviations i
Objective of the Report iii
Scope of this Report v
About the Authors vii
What We Got Right, What We Got Wrong ix
3-Page Summary xi
Executive Summary xiv
CHAPTER 1 — CONTEXT
1.1 Global AI Infrastructure and Investment Landscape 33
1.2 US and Chinese Hyperscaler CapEx Trends and Projections 39
1.3 AI Regulatory Landscape and Export Controls 45
1.4 The US–China Technology Divide 51
CHAPTER 2 — MARKET FORECASTS
2.1 Processor Revenue Forecast 57
2.1.1 Total Data Center Processor Market, 2021–2040 ($B) 59
2.1.2 GPU Revenue Forecast, 2021–2040 ($B) 64
2.1.3 AI ASIC Revenue Forecast, 2021–2040 ($B) 68
2.1.4 Server CPU Revenue Forecast, 2021–2040 ($B) 71
2.1.5 FPGA Data Center Revenue Forecast, 2021–2040 ($M) 74
2.2 Average Selling Price (ASP) Forecast 77
2.2.1 GPU ASP Trends by Product Tier, 2021–2040 ($K) 78
2.2.2 AI ASIC ASP Trends by Hyperscaler, 2021–2040 ($K) 81
2.2.3 CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 84
2.3 Processor Volume Forecast 87
2.3.1 GPU Unit Shipments by Vendor, 2021–2040 (K units) 88
2.3.2 AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units) 92
2.3.3 CPU Unit Shipments by Vendor, 2021–2040 (M units) 96
2.4 Wafer Forecast 101
2.4.1 GPU & AI ASIC Wafer Starts by Technology Node, 2021–2040 102
2.4.2 Wafer Starts by Foundry (TSMC, Samsung, Intel Foundry) 106
2.4.3 GPU & AI ASIC Compute Die Forecast, 2021–2040 109
2.4.4 HBM-Driven Revenue Separation from GPU & AI ASIC 112
2.5 Server Tray Volume Forecast 115
2.6 CPU Focus 121
2.7 GPU & AI ASIC Focus 127
CHAPTER 3 — MARKET TRENDS
3.1 Cost of Generative AI Inference and Training 137
3.2 From Agentic AI to Physical AI 148
3.3 Recommendation Models for Social Networks 158
3.4 Coding Assistants 164
3.5 Search Engine vs. LLM 170
3.6 OpenClaw 176
3.7 CapEx vs. OpEx in the Era of Generative AI 181
3.8 Is the Future of AI Data Centers in Space? 193
CHAPTER 4 — MARKET SHARE & SUPPLY CHAIN
4.1 Data Center Ecosystem Map 203
4.2 Foundation Models Ecosystem Map 212
4.3 U.S. vs. China Tech War — Timeline 219
4.4 Financial Metrics of Data Center Chip Designers 227
4.5 Case Study: OpenAI Revenue and Gigawatt 238
4.6 Market Share: CPU, GPU, AI ASIC & XPU Co-Designers 246
4.6.1 GPU Market Share by Revenue and Units 247
4.6.2 AI ASIC Market Share by Hyperscaler 251
4.6.3 CPU Market Share by Vendor 255
4.6.4 XPU Co-Designer Revenue Market Share 259
4.7 Focus on Mainland China 263
4.7.1 Chinese DC Processor Market Size & Forecast 264
4.7.2 Chinese Hyperscaler Processor Demand 267
4.7.3 Chinese Processor Manufacturer Profiles & Roadmaps 271
4.7.4 China DC Processor Supply Chain 274
CHAPTER 5 — TECHNOLOGY TRENDS
5.1 CPU Technology Trends 279
5.1.1 x86 Architecture Evolution 281
5.1.2 Arm-Based CPU Momentum in the Data Center 285
5.1.3 RISC-V in the Data Center 289
5.1.4 CPU Specialisation for AI Workloads 293
5.2 GPU Technology Trends 299
5.2.1 Process Node Roadmap and Transition 300
5.2.2 Chiplet and Multi-Die Architectures 304
5.2.3 Rack-Scale GPU Architectures (NVL72 and Beyond) 308
5.2.4 Memory Bandwidth and HBM Integration 313
5.2.5 Networking and Interconnect Evolution 317
5.3 AI ASIC Technology Trends 323
5.3.1 Hyperscaler ASIC Product Roadmaps 324
5.3.2 AI ASIC Start-Up Landscape 330
5.3.3 AI ASIC Technology Specification Database 335
5.3.4 Compute Disaggregation for AI Inference 341
5.4 GPU vs. AI ASIC: Comparative Analysis 347
5.5 Advanced Packaging and HBM Memory 355
5.5.1 HBM Technology Roadmap (HBM2E to HBM4) 356
5.5.2 CoWoS and Advanced Packaging Capacity 360
5.5.3 Custom HBM and Co-Design Trends 363
5.5.4 AI Rack Bill of Materials 366
5.6 Emerging Computing Architectures 371
5.6.1 Photonic Computing 372
5.6.2 Neuromorphic Computing 374
5.6.3 Quantum Computing Outlook 376
CHAPTER 6 — OUTLOOK
6.1 Market Outlook 2026–2040 381
6.2 Technology Outlook 2026–2040 386
6.3 Key Risks and Opportunities 390
6.4 Strategic Recommendations 394
CHAPTER 7 — COMPANY PROFILES 399-480 (81 company profiles)
List of Figures
Fig. 1.1 Global AI Infrastructure Investment Forecast, 2021–2040 ($B) 34
Fig. 1.2 US vs. Chinese Hyperscaler CapEx, 2021–2040 ($B) 39
Fig. 1.3 Data Center Power Consumption Forecast, 2024–2040 (GW) 40
Fig. 1.4 AI-Related Data Center Construction Starts by Region, 2022–2028 42
Fig. 1.5 US Export Controls on AI Chips — Key Milestones, 2019–2026 46
Fig. 1.6 US–China Technology Decoupling Timeline, 2018–2026 51
Fig. 2.1 Total Data Center Processor Market Revenue Forecast, 2021–2040 ($B) 60
Fig. 2.2 Revenue Breakdown by Processor Type (CPU, GPU, AI ASIC, FPGA), 2021–2040 61
Fig. 2.3 Data Center Processor CAGR by Category, 2025–2040 (%) 62
Fig. 2.4 GPU Market Revenue Forecast, 2021–2040 ($B) 64
Fig. 2.5 GPU Revenue Split by Vendor (Nvidia, AMD, Others), 2021–2040 65
Fig. 2.6 Nvidia GPU Revenue by Product Generation, 2021–2028 ($B) 66
Fig. 2.7 AMD GPU Revenue by Product Generation, 2021–2028 ($B) 67
Fig. 2.8 AI ASIC Market Revenue Forecast, 2021–2040 ($B) 68
Fig. 2.9 AI ASIC Revenue Split by Hyperscaler, 2021–2040 69
Fig. 2.10 Server CPU Market Revenue Forecast, 2021–2040 ($B) 71
Fig. 2.11 Server CPU Revenue Split by Architecture (x86 vs. Arm), 2021–2040 72
Fig. 2.12 FPGA Data Center Revenue Forecast, 2021–2040 ($M) 74
Fig. 2.13 GPU ASP Evolution by Product Tier, 2021–2040 ($K) 78
Fig. 2.14 AI ASIC ASP Trends by Hyperscaler, 2021–2040 ($K) 81
Fig. 2.15 Server CPU ASP Trends — Intel Xeon vs. AMD EPYC, 2021–2040 ($) 84
Fig. 2.16 GPU Unit Shipments by Vendor, 2021–2040 (K units) 88
Fig. 2.17 Nvidia GPU Unit Shipments by Product Generation, 2021–2028 89
Fig. 2.18 AMD GPU Unit Shipments by Product Generation, 2021–2028 90
Fig. 2.19 AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units) 92
Fig. 2.20 Google TPU Unit Deployment Forecast, 2021–2040 93
Fig. 2.21 AWS Trainium & Inferentia Unit Forecast, 2021–2040 94
Fig. 2.22 Microsoft MAIA Unit Forecast, 2021–2040 95
Fig. 2.23 CPU Unit Shipments — Data Center, 2021–2040 (M units) 96
Fig. 2.24 Intel vs. AMD CPU Market Share in Unit Terms, 2021–2040 (%) 97
Fig. 2.25 Hyperscaler Custom CPU Unit Adoption, 2022–2040 (M units) 98
Fig. 2.26 GPU & AI ASIC Wafer Starts by Technology Node, 2021–2040 (KW/month) 102
Fig. 2.27 Wafer Consumption Split: Advanced Nodes (<5nm, 5nm, 7nm), 2021–2040 103
Fig. 2.28 GPU & AI ASIC Wafer Starts by Foundry, 2021–2040 106
Fig. 2.29 TSMC Advanced Node Capacity Forecast, 2024–2040 (KW/month) 107
Fig. 2.30 GPU & AI ASIC Compute Die Forecast, 2021–2040 109
Fig. 2.31 Average Die Size Trend — GPU vs. AI ASIC, 2021–2040 (mm²) 110
Fig. 2.32 HBM Revenue Separated from GPU & AI ASIC Total, 2021–2040 ($B) 112
Fig. 2.33 AI Server vs. General-Purpose Server Tray Volume, 2021–2040 (M units) 115
Fig. 2.34 AI Server Rack Configuration and Architecture, 2025–2040 117
Fig. 2.35 CPU Market Share by Revenue — Intel vs. AMD vs. Arm-based, 2021–2040 121
Fig. 2.36 Hyperscaler Arm CPU Deployment Ramp, 2022–2040 122
Fig. 2.37 CPU Product Roadmap — Intel, AMD, Arm, Google, AWS, Nvidia, 2024–2030 124
Fig. 2.38 GPU Market Share by Revenue, 2021–2040 (%) 127
Fig. 2.39 AI ASIC Market Share by Deployment Volume, 2021–2040 (%) 129
Fig. 2.40 GPU & AI ASIC Split by Technology Node, 2021–2040 131
Fig. 3.1 Cost per Token Trend — Training and Inference, 2021–2040 ($/M tokens) 139
Fig. 3.2 Training Compute Requirements by Model Type, 2020–2028 (FLOPs) 141
Fig. 3.3 Inference Cost Breakdown by Infrastructure Component, 2025 (%) 143
Fig. 3.4 Token Cost Reduction Roadmap, 2025–2040 ($/M tokens) 145
Fig. 3.5 AI Model Parameter Count vs. Hardware Requirements, 2020–2028 147
Fig. 3.6 Agentic AI Market Taxonomy and Use Cases 152
Fig. 3.7 AI Agent Deployment Forecast by Sector, 2025–2040 154
Fig. 3.8 Physical AI Hardware Requirements vs. Generative AI, 2025–2040 156
Fig. 3.9 Robotics Semiconductor Market Forecast, 2024–2040 ($B) 158
Fig. 3.10 Recommendation Model Architecture Evolution, 2018–2028 164
Fig. 3.11 Recommendation Model Compute Demand by Platform, 2024–2040 166
Fig. 3.12 AI-Powered Coding Assistant Market Share, 2024–2028 (%) 171
Fig. 3.13 Coding AI GPU Compute Demand, 2024–2040 173
Fig. 3.14 LLM vs. Traditional Search: Query Volume Forecast, 2022–2040 178
Fig. 3.15 AI Search Compute Infrastructure Requirements, 2024–2040 180
Fig. 3.16 CapEx Cycle — US Hyperscalers, 2015–2040 ($B) 182
Fig. 3.17 CapEx-to-Revenue Ratio — Major Hyperscalers, 2020–2040 (%) 184
Fig. 3.18 AI Infrastructure OpEx vs. CapEx Split, 2024–2040 186
Fig. 3.19 Cloud AI Chip Rental vs. Ownership Economics, 2025–2040 188
Fig. 3.20 Space-Based Data Center Conceptual Architecture 201
Fig. 3.21 Low Earth Orbit Latency and Bandwidth Projections, 2025–2035 203
Fig. 4.1 Global Data Center Processor Ecosystem Map 203
Fig. 4.2 AI Chip Supply Chain — From Silicon to Hyperscaler 205
Fig. 4.3 Co-Designer and Hyperscaler Relationship Map 208
Fig. 4.4 OSAT and Advanced Packaging Supply Chain Map 210
Fig. 4.5 Foundation Models Ecosystem Map — Developers and Infrastructure 212
Fig. 4.6 Open vs. Closed Source AI Model Landscape, 2024 215
Fig. 4.7 Foundation Model Training Infrastructure by Developer 217
Fig. 4.8 US Export Control Timeline — Semiconductors, 2018–2026 219
Fig. 4.9 Chinese AI Chip Import Replacement Progress, 2022–2028 (%) 222
Fig. 4.10 Sanctioned vs. Unsanctioned Chinese AI Chip Revenues, 2022–2028 225
Fig. 4.11 Comparative Revenue — Data Center Chip Designers, 2021–2025 ($B) 227
Fig. 4.12 Gross Margin Comparison — Nvidia vs. AMD vs. Intel, 2020–2025 (%) 229
Fig. 4.13 R&D Spend as % of Revenue — Key Chip Designers, 2020–2025 231
Fig. 4.14 AI Semiconductor Start-Up Fundraising, 2019–Q1 2026 ($M) 234
Fig. 4.15 OpenAI Revenue Forecast, 2023–2030 ($B) 238
Fig. 4.16 OpenAI Compute Demand (Gigawatt), 2023–2030 240
Fig. 4.17 OpenAI GPU Procurement Forecast by Generation, 2023–2028 242
Fig. 4.18 GPU Market Share by Revenue, 2021–2025 (%) 247
Fig. 4.19 GPU Market Share by Units, 2021–2025 (%) 248
Fig. 4.20 Nvidia, AMD, Google, AWS GPU/ASIC Unit Split, 2021–2028 250
Fig. 4.21 AI ASIC Market Share by Hyperscaler, 2021–2025 (%) 251
Fig. 4.22 XPU Co-Designer Revenue — Broadcom, Marvell, MediaTek, Alchip, GUC, 2023–2026 252
Fig. 4.23 CPU Market Share by Revenue — Intel vs. AMD vs. Arm, 2021–2025 (%) 255
Fig. 4.24 Hyperscaler Custom CPU Market Share Evolution, 2022–2028 257
Fig. 4.25 XPU Co-Designer Revenue Share — Broadcom, Marvell, Others, 2021–2026 259
Fig. 4.26 Chinese DC Processor Market Size, 2021–2028 ($B) 264
Fig. 4.27 Chinese Hyperscaler Processor Demand Forecast, 2021–2028 267
Fig. 4.28 Chinese Processor Maker Market Share (Unit), 2024 & 2025 269
Fig. 4.29 HiSilicon, Cambricon, Baidu, Hygon DC Processor Roadmap 271
Fig. 4.30 China DC Processor Supply Chain Map 274
Fig. 5.1 CPU Architecture Comparison — x86, Arm, RISC-V for the Data Center 282
Fig. 5.2 Arm Server CPU Shipment Forecast, 2022–2040 (M units) 286
Fig. 5.3 RISC-V Data Center Adoption Forecast, 2025–2040 290
Fig. 5.4 CPU Specialisation for AI Inference Workloads 293
Fig. 5.5 GPU Process Node Roadmap — Nvidia, AMD, 2020–2030 300
Fig. 5.6 GPU Die Size Evolution and Chiplet Transition, 2020–2030 (mm²) 304
Fig. 5.7 Rack-Scale GPU Architecture — NVL72 and Next-Generation Platforms 308
Fig. 5.8 GPU Memory Bandwidth Trend — HBM Generations, 2020–2030 (TB/s) 313
Fig. 5.9 NVLink and Interconnect Bandwidth Evolution, 2020–2030 317
Fig. 5.10 Hyperscaler ASIC Roadmap Comparison — Google, AWS, Microsoft, Meta 324
Fig. 5.11 AI ASIC Start-Up Landscape by Funding Stage, 2024 330
Fig. 5.12 AI ASIC Technology Specification Matrix (Selected Companies) 335
Fig. 5.13 Disaggregated Inference Architecture Diagram 341
Fig. 5.14 GPU vs. AI ASIC: Performance per Watt Comparison, 2022–2026 347
Fig. 5.15 GPU vs. AI ASIC: Training vs. Inference Suitability Matrix 349
Fig. 5.16 GPU vs. AI ASIC: Total Cost of Ownership Analysis 351
Fig. 5.17 HBM Technology Roadmap — HBM2E to HBM4, 2020–2028 356
Fig. 5.18 HBM Bandwidth and Capacity per Stack by Generation, 2020–2028 357
Fig. 5.19 CoWoS Capacity Expansion Roadmap — TSMC, 2022–2028 360
Fig. 5.20 Advanced Packaging Market Share — CoWoS, SoIC, Others, 2024–2028 362
Fig. 5.21 Custom HBM Co-Design Relationships Map 363
Fig. 5.22 AI Server Rack Bill of Materials — Component Breakdown, 2025 ($K) 366
Fig. 5.23 AI Rack BoM Cost Evolution, 2023–2028 ($K) 368
Fig. 5.24 Silicon Photonics Market Forecast in Data Centers, 2024–2040 ($B) 372
Fig. 5.25 Neuromorphic Computing Roadmap, 2024–2040 374
Fig. 5.26 Quantum Computing Timeline to Commercial Viability, 2025–2040 376
Fig. 6.1 Data Center Processor Market Scenario Analysis, 2026–2040 ($B) 382
Fig. 6.2 Bull, Base, Bear Case Revenue Scenarios by Processor Type, 2040 384
Fig. 6.3 Technology Roadmap Summary — CPU, GPU, AI ASIC, 2026–2040 386
Fig. 6.4 Competitive Landscape Risk Matrix, 2026–2040 390
Fig. 6.5 Investment Opportunity Map — Data Center Semiconductor Ecosystem 393
圖表清單 List of Tables & Figures
List of Tables
Table 2.1 Data Center Processor Market Revenue Summary, 2021–2040 ($B) 60
Table 2.2 GPU Revenue by Vendor, 2021–2040 ($B) 65
Table 2.3 AI ASIC Revenue by Hyperscaler, 2021–2040 ($B) 69
Table 2.4 Server CPU Revenue by Vendor, 2021–2040 ($B) 72
Table 2.5 GPU ASP by Product Tier, 2021–2040 ($K) 79
Table 2.6 AI ASIC ASP by Hyperscaler, 2021–2040 ($K) 82
Table 2.7 GPU Unit Shipments by Vendor, 2021–2040 (K units) 89
Table 2.8 AI ASIC Unit Shipments by Hyperscaler, 2021–2040 (K units) 93
Table 2.9 CPU Unit Shipments by Vendor, 2021–2040 (M units) 97
Table 2.10 GPU & AI ASIC Wafer Starts by Node and Foundry, 2021–2040 103
Table 2.11 AI Server vs. General-Purpose Server Tray Volume, 2021–2040 (M units) 116
Table 2.12 CPU Processor Roadmap Summary — Major Vendors, 2024–2030 125
Table 2.13 GPU & AI ASIC Product Roadmap Summary, 2024–2030 133
Table 3.1 Cost per Token by Model Size and Hardware Configuration, 2024–2040 140
Table 3.2 Agentic AI Use Cases by Industry and Hardware Requirements 153
Table 3.3 Coding Assistant Market Share and Underlying Infrastructure, 2024 172
Table 4.1 Financial Metrics — Top 10 Data Center Chip Designers, 2021–2025 228
Table 4.2 US and Chinese Hyperscaler CapEx Summary, 2021–2026 ($B) 233
Table 4.3 AI Semiconductor Start-Up Fundraising Database, 2019–Q1 2026 235
Table 4.4 GPU Market Share Summary by Revenue and Units, 2021–2025 248
Table 4.5 AI ASIC Specifications — Google, AWS, Microsoft, Meta, 2024–2026 253
Table 4.6 Chinese Data Center Processor Manufacturer Overview 272
Table 4.7 China DC Processor Supply Chain — Key Component Suppliers 274
Table 5.1 CPU Specifications — Intel, AMD, AWS, Google, Microsoft, Huawei, Nvidia, 2024–2026 284
Table 5.2 GPU Specifications — Nvidia Blackwell, Rubin; AMD MI350X, MI450, 2024–2026 302
Table 5.3 AI ASIC Technology Specification Database (Full, All Major Vendors) 336
Table 5.4 HBM Specification Comparison — HBM2E, HBM3, HBM3E, HBM4 357
Table 5.5 AI Server Rack BoM — Itemised Cost Breakdown, 2025 ($K) 367
Table 5.6 Emerging Computing Technology Readiness Assessment 377
Table 6.1 Market Forecast Summary — Bull / Base / Bear Scenarios, 2026–2040 ($B) 383
Table 6.2 Key Risk Register — Probability and Impact Assessment 391
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