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市場調查報告書
商品編碼
1918017

加速卡市場 - 2026-2031 年預測

Accelerator Card Market - Forecast from 2026 to 2031
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 141 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

預計加速卡市場將從 2025 年的 124.32 億美元成長到 2031 年的 196.99 億美元，複合年成長率為 7.97%。

加速卡（包括GPU、TPU、FPGA和客製化ASIC在內的專用平行處理硬體）已成為任何需要大規模浮點或整數吞吐量的工作負載的基礎建置模組。雖然消費級遊戲GPU仍佔據重要地位，但目前大部分新銷售量和幾乎所有高收益收入都來自資料中心、雲端運算和邊緣推理應用。

雲端加速器是成長最快、價值最高的細分市場。超大規模資料中心業者（AWS、微軟 Azure、Google雲端、阿里雲、騰訊雲端）和二線雲端服務供應商正從通用 CPU 執行個體轉向以 GPU、TPU 和客製化晶片為主的異質運算叢集。訓練大規模語言模型（100 億至 1750 億以上參數）、大規模推理、影片轉碼、科學模擬和基因組學等應用都充分展現了加速器連接性和近乎完美的彈性。雲端服務供應商正擴大提供多執行個體 GPU 分區（MIG、MPS）和裸機加速器的存取權限，以最大限度地提高資源利用率和收費效率。

北美在消費和創新方面持續保持主導。作為英偉達、AMD、英特爾、Google（TPU）以及幾乎所有主流雲端服務供應商的總部所在地，該地區擁有無與倫比的研發速度和領先應用優勢。成熟的資料中心基礎設施、較高的能源成本承受能力以及大規模的遊戲和專業視覺化使用者群體，共同構成了一個良性循環的需求成長點。遊戲仍然是重要的第二大驅動力，高階消費級顯示卡（RTX 4090 系列）經常被重新用於小規模訓練和推理叢集。

建築風格的演變分化為兩條截然不同的發展軌跡：

1. 通用 GPU 運算：NVIDIA 的 Hopper (H100/H200) 和 Blackwell 平台繼續為混合精度訓練和大規模批量推理設定成本績效基準，而 AMD Instinct MI300X 和 Intel Gaudi3 則旨在特定工作負載中獲得性價比優勢。

2. 特定領域的加速器：Google TPU v5p、AWS Trainium/Inferentia、Microsoft Maia、Meta MTIA 和眾多Start-Ups的ASIC 旨在最佳化推理密集型或高度規律性工作負載的總擁有成本，在這些工作負載中，柔軟性可以換取效率。

功率密度和散熱正成為關鍵的物理限制因素。現代旗艦加速器每個面板的功率通常超過 700-1000W，這要求資料中心採用晶片級液冷和 48-54V 機架式配電。資料中心營運商現在評估解決方案的標準是「每瓦每美元的效能」以及 3-5 年折舊免稅額週期內的總擁有成本。

競爭格局有利於垂直整合型企業，它們能夠統一控制整個晶片和軟體堆疊（CUDA、ROCm、Triton、OpenXLA）。雖然通用GPU供應商仍然主導訓練領域，但推理領域正日益多元化，轉向客製化晶片，因為在這些領域，能源效率和記憶體頻寬至關重要。基於FPGA的加速器（例如Xilinx Alveo、Intel Agilex）在低延遲金融、基因組學和訊號處理等領域佔有一席之地，在這些領域，可重構性使其較高的單價物有所值。

供應鏈韌性已成為董事會層面的優先事項。儘管先進封裝技術（CoWoS-S、InFO、HDAP）和HBM3/HBM3E記憶體生產集中在台灣和韓國，且美國《晶片法案》和歐盟《晶片法案》的資金正在推動地域多元化，但產能的大幅提升預計仍需24-36個月。

對於企業架構師和採購團隊而言，加速器選擇目前依賴總體擁有成本 (TCO) 模型，該模型考慮了實例利用率、軟體生態系統鎖定、電力/冷卻基礎設施成本以及預期使用壽命。雖然雲端市場已基本實現了訓練的商品化，但推理仍然高度分散，存在本地客製化晶片、雲端 GPU 實例和邊緣最佳化硬體等多種選擇。

整體而言，加速卡具有顯著的架構優勢：它們是經濟高效地擴展現代人工智慧/機器學習工作負載的唯一可行方案，這得益於生成式人工智慧、雲端遷移和科學運算等長期發展趨勢，以及架構複雜性不斷擴大的領先者和追隨者之間的差距。那些掌控效能最高節點、最深厚的軟體生態系統和最高效客製化晶片的公司，可望在這個關鍵的運算基礎設施領域實現持續30-7.97%的複合年成長率和超過50%的營運利潤率。

本報告的主要優勢：

  • 深入分析：取得以客戶群、政府政策和社會經濟因素、消費者偏好、垂直產業和其他細分市場為重點的深入市場洞察，涵蓋主要地區和新興地區。
  • 競爭格局：了解主要企業採取的策略舉措，並了解透過正確的策略打入市場的潛力。
  • 市場促進因素與未來趨勢：探索動態因素和關鍵市場趨勢，以及它們將如何塑造未來的市場發展。
  • 可執行的建議：利用洞察力為策略決策提供訊息，從而在動態環境中開拓新的業務管道和收入來源。
  • 受眾範圍廣：對新興企業、研究機構、顧問公司、中小企業和大型企業都有益處且經濟高效。

它是用來做什麼的？

產業與市場洞察、商業機會評估、產品需求預測、打入市場策略、地理擴張、資本投資決策、法律規範及其影響、新產品開發、競爭影響

分析範圍

  • 歷史資料（2021-2025 年）和預測資料（2026-2031 年）
  • 成長機會、挑戰、供應鏈前景、法規結構、客戶行為和趨勢分析
  • 競爭對手定位、策略和市場佔有率分析
  • 按業務板塊和地區（國家）分類的收入成長和預測分析
  • 公司概況（策略、產品、財務資訊、關鍵趨勢等）

目錄

第1章執行摘要

第2章市場概述

  • 市場概覽
  • 市場定義
  • 分析範圍
  • 市場區隔

第3章 商業情境

  • 市場促進因素
  • 市場限制
  • 市場機遇
  • 波特五力分析
  • 產業價值鏈分析
  • 政策和法規
  • 策略建議

第4章 技術展望

第5章 加速卡片市場（按類型分類）

  • 介紹
  • 高效能運算加速器
  • 雲加速器

第6章 加速卡片市場（依應用領域分類）

  • 介紹
  • 用於深度學習訓練
  • 公共雲端介面
  • 企業介面

第7章 依處理器類型分類的加速卡市場

  • 介紹
  • CPU（中央處理器）
  • GPU（影像處理單元）
  • FPGA（現場可程式化閘陣列）
  • ASIC（專用積體電路）

第8章 各地區的加速卡市場

  • 介紹
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 南美洲
    • 巴西
    • 阿根廷
    • 其他
  • 歐洲
    • 德國
    • 法國
    • 英國
    • 西班牙
    • 其他
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 其他
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 韓國
    • 印尼
    • 泰國
    • 其他

第9章：競爭格局與分析

  • 主要企業和策略分析
  • 市佔率分析
  • 企業合併、協議、商業合作
  • 競爭對手儀錶板

第10章：公司簡介

  • NVIDIA Corporation
  • Intel Corporation
  • Advanced Micro Devices, Inc.
  • Achronix Semiconductor Corporation
  • Oracle
  • Xilinx
  • IBM
  • Hewlett Packard Enterprise Development LP
  • Dell

第11章附錄

  • 貨幣
  • 先決條件
  • 基準年和預測年時間表
  • 相關人員的主要收益
  • 分析方法
  • 簡稱
簡介目錄
Product Code: KSI061615406

Accelerator Card Market is projected to expand at a 7.97% CAGR, attaining USD 19.699 billion in 2031 from USD 12.432 billion in 2025.

Accelerator cards-specialized parallel-processing hardware encompassing GPUs, TPUs, FPGAs, and custom ASICs-have become the foundational building block for any workload requiring massive floating-point or integer throughput. While consumer-grade gaming GPUs remain highly visible, the majority of new unit volume and virtually all high-margin revenue now originates from data-center, cloud, and edge-inference applications.

Cloud accelerators represent the fastest-growing and highest-value segment. Hyperscalers (AWS, Microsoft Azure, Google Cloud, Alibaba, Tencent) and second-tier providers have shifted from general-purpose CPU instances to heterogeneous compute fleets dominated by GPU, TPU, and custom silicon. Training of large language models (10B-175B+ parameters), inference at scale, video transcoding, scientific simulation, and genomics all exhibit near-perfect elasticity with accelerator attach rates. Cloud providers increasingly offer multi-instance GPU partitioning (MIG, MPS) and bare-metal accelerator access to maximize utilization and billing efficiency.

North America continues to dominate both consumption and innovation. The region hosts the headquarters of NVIDIA, AMD, Intel, Google (TPU), and virtually all major cloud providers, giving it unmatched R&D velocity and first-mover deployment advantage. Mature data-center infrastructure, high electricity cost tolerance, and a massive installed base of gaming and professional-visualization users create a self-reinforcing demand flywheel. Gaming remains a meaningful secondary driver, with high-end consumer cards (RTX 4090-class) frequently repurposed for small-scale training and inference clusters.

Architecture evolution has bifurcated into two distinct trajectories:

1. General-purpose GPU compute-NVIDIA's Hopper (H100/H200) and Blackwell platforms continue to set the performance-per-dollar benchmark for mixed-precision training and large-batch inference, while AMD Instinct MI300X and Intel Gaudi3 target price-performance leadership in specific workloads.

2. Domain-specific accelerators-Google TPU v5p, AWS Trainium/Inferentia, Microsoft Maia, Meta MTIA, and numerous startup ASICs optimize total-cost-of-ownership for inference-heavy or highly regular workloads where flexibility can be traded for efficiency.

Power density and cooling have emerged as the primary physical constraints. Modern flagship accelerators routinely exceed 700-1000 W per card, pushing facilities toward direct-to-chip liquid cooling and 48-54 V rack power distribution. Data-center operators now evaluate solutions on performance-per-watt-per-dollar and total-cost-of-ownership over three-to-five-year depreciation cycles.

Competitive dynamics increasingly favor vertically integrated players who control both silicon and the full software stack (CUDA, ROCm, Triton, OpenXLA). While merchant GPU vendors still dominate training, inference is fragmenting toward custom silicon where power efficiency and memory bandwidth are paramount. FPGA-based accelerators (Xilinx Alveo, Intel Agilex) retain niches in low-latency finance, genomics, and signal processing where reconfigurability justifies higher unit cost.

Supply-chain resilience has become a board-level priority. Concentration of advanced packaging (CoWoS-S, InFO, HDAP) and HBM3/HBM3E memory production in Taiwan and South Korea, combined with U.S. CHIPS Act and EU Chips Act funding, is driving geographic diversification, but meaningful capacity additions remain 24-36 months away.

For enterprise architects and procurement teams, accelerator selection now hinges on total-cost-of-ownership models that factor instance utilization, software ecosystem lock-in, power/cooling infrastructure cost, and expected useful life. Cloud marketplaces have largely commoditized training, while inference remains highly fragmented between on-premise custom silicon, cloud GPU instances, and edge-optimized hardware.

Overall, accelerator cards occupy an unassailable structural position: the only viable path to economically scaling modern AI/ML workloads, secular tailwinds from generative AI, cloud migration, and scientific computing, and architectural complexity that continues to widen the gap between leaders and followers. Companies controlling the highest-performance nodes, deepest software ecosystems, and most efficient custom silicon are positioned for sustained 30-7.97% CAGR and operating margins exceeding 50 % in this defining compute infrastructure category.

Key Benefits of this Report:

  • Insightful Analysis: Gain detailed market insights covering major as well as emerging geographical regions, focusing on customer segments, government policies and socio-economic factors, consumer preferences, industry verticals, and other sub-segments.
  • Competitive Landscape: Understand the strategic maneuvers employed by key players globally to understand possible market penetration with the correct strategy.
  • Market Drivers & Future Trends: Explore the dynamic factors and pivotal market trends and how they will shape future market developments.
  • Actionable Recommendations: Utilize the insights to exercise strategic decisions to uncover new business streams and revenues in a dynamic environment.
  • Caters to a Wide Audience: Beneficial and cost-effective for startups, research institutions, consultants, SMEs, and large enterprises.

What do businesses use our reports for?

Industry and Market Insights, Opportunity Assessment, Product Demand Forecasting, Market Entry Strategy, Geographical Expansion, Capital Investment Decisions, Regulatory Framework & Implications, New Product Development, Competitive Intelligence

Report Coverage:

  • Historical data from 2021 to 2025 & forecast data from 2026 to 2031
  • Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, and Trend Analysis
  • Competitive Positioning, Strategies, and Market Share Analysis
  • Revenue Growth and Forecast Assessment of segments and regions including countries
  • Company Profiling (Strategies, Products, Financial Information), and Key Developments among others.

Segmentation:

  • By Type
  • HPC Accelerator
  • Cloud Accelerator
  • By Application
  • Deep Learning Training
  • Public Cloud Interface
  • Enterprise Interface
  • By Processor Type
  • Central Processing Units (CPU)
  • Graphics Processing Units (GPU)
  • Field-Programmable Gate Arrays (FPGA)
  • Application-specific Integrated Circuit (ASIC)
  • By Geography
  • North America
  • USA
  • Canada
  • Mexico
  • South America
  • Brazil
  • Argentina
  • Others
  • Europe
  • Germany
  • France
  • United Kingdom
  • Spain
  • Others
  • Middle East and Africa
  • Saudi Arabia
  • UAE
  • Others
  • Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Indonesia
  • Thailand
  • Others

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

  • 2.1. Market Overview
  • 2.2. Market Definition
  • 2.3. Scope of the Study
  • 2.4. Market Segmentation

3. BUSINESS LANDSCAPE

  • 3.1. Market Drivers
  • 3.2. Market Restraints
  • 3.3. Market Opportunities
  • 3.4. Porter's Five Forces Analysis
  • 3.5. Industry Value Chain Analysis
  • 3.6. Policies and Regulations
  • 3.7. Strategic Recommendations

4. TECHNOLOGICAL OUTLOOK

5. ACCELERATOR CARD MARKET BY TYPE

  • 5.1. Introduction
  • 5.2. HPC Accelerator
  • 5.3. Cloud Accelerator

6. ACCELERATOR CARD MARKET BY APPLICATION

  • 6.1. Introduction
  • 6.2. Deep Learning Training
  • 6.3. Public Cloud Interface
  • 6.4. Enterprise Interface

7. ACCELERATOR CARD MARKET BY PROCESSOR TYPE

  • 7.1. Introduction
  • 7.2. Central Processing Units (CPU)
  • 7.3. Graphics Processing Units (GPU)
  • 7.4. Field-Programmable Gate Arrays (FPGA)
  • 7.5. Application-specific Integrated Circuit (ASIAC)

8. ACCELERATOR CARD MARKET BY GEOGRAPHY

  • 8.1. Introduction
  • 8.2. North America
    • 8.2.1. USA
    • 8.2.2. Canada
    • 8.2.3. Mexico
  • 8.3. South America
    • 8.3.1. Brazil
    • 8.3.2. Argentina
    • 8.3.3. Others
  • 8.4. Europe
    • 8.4.1. Germany
    • 8.4.2. France
    • 8.4.3. United Kingdom
    • 8.4.4. Spain
    • 8.4.5. Others
  • 8.5. Middle East and Africa
    • 8.5.1. Saudi Arabia
    • 8.5.2. UAE
    • 8.5.3. Others
  • 8.6. Asia Pacific
    • 8.6.1. China
    • 8.6.2. India
    • 8.6.3. Japan
    • 8.6.4. South Korea
    • 8.6.5. Indonesia
    • 8.6.6. Thailand
    • 8.6.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 9.1. Major Players and Strategy Analysis
  • 9.2. Market Share Analysis
  • 9.3. Mergers, Acquisitions, Agreements, and Collaborations
  • 9.4. Competitive Dashboard

10. COMPANY PROFILES

  • 10.1. NVIDIA Corporation
  • 10.2. Intel Corporation
  • 10.3. Advanced Micro Devices, Inc.
  • 10.4. Achronix Semiconductor Corporation
  • 10.5. Oracle
  • 10.6. Xilinx
  • 10.7. IBM
  • 10.8. Hewlett Packard Enterprise Development LP
  • 10.9. Dell

11. APPENDIX

  • 11.1. Currency
  • 11.2. Assumptions
  • 11.3. Base and Forecast Years Timeline
  • 11.4. Key Benefits for the Stakeholders
  • 11.5. Research Methodology
  • 11.6. Abbreviations