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市场调查报告书
商品编码
1918017

加速卡市场 - 2026-2031 年预测

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个工作天内

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简介目录

预计加速卡市场将从 2025 年的 124.32 亿美元成长到 2031 年的 196.99 亿美元，复合年增长率为 7.97%。

加速卡（包括GPU、TPU、FPGA和客製化ASIC在内的专用平行处理硬体）已成为任何需要大规模浮点或整数吞吐量的工作负载的基础建置模组。虽然消费级游戏GPU仍占据重要地位，但目前大部分新销售量和几乎所有高收益收入都来自资料中心、云端运算和边缘推理应用。

云端加速器是成长最快、价值最高的细分市场。超大规模资料中心业者（AWS、微软 Azure、Google云端、阿里云、腾讯云端）和二线云端服务供应商正从通用 CPU 执行个体转向以 GPU、TPU 和客製化晶片为主的异质运算丛集。训练大规模语言模型（100 亿至 1750 亿以上参数）、大规模推理、影片转码、科学模拟和基因组学等应用都充分展现了加速器连接性和近乎完美的弹性。云端服务供应商正越来越多地提供多执行个体 GPU 分区（MIG、MPS）和裸机加速器的存取权限，以最大限度地提高资源利用率和收费效率。

北美在消费和创新方面持续保持主导。作为英伟达、AMD、英特尔、Google（TPU）以及几乎所有主流云端服务供应商的总部所在地，该地区拥有无与伦比的研发速度和领先应用优势。成熟的资料中心基础设施、较高的能源成本承受能力以及大规模的游戏和专业视觉化使用者群体，共同构成了一个良性循环的需求成长点。游戏仍然是重要的第二大驱动力，高阶消费级显示卡（RTX 4090 系列）经常被重新用于小规模训练和推理丛集。

建筑风格的演变分化为两条截然不同的发展轨迹：

1. 通用 GPU 运算：NVIDIA 的 Hopper (H100/H200) 和 Blackwell 平台继续为混合精度训练和大规模批量推理设定成本绩效基准，而 AMD Instinct MI300X 和 Intel Gaudi3 则旨在特定工作负载中获得性价比优势。

2. 特定领域的加速器：Google TPU v5p、AWS Trainium/Inferentia、Microsoft Maia、Meta MTIA 和众多Start-Ups的ASIC 旨在优化推理密集型或高度规律性工作负载的总拥有成本，在这些工作负载中，柔软性可以换取效率。

功率密度和散热正成为关键的物理限制因素。现代旗舰加速器每个面板的功率通常超过 700-1000W，这要求资料中心采用晶片级液冷和 48-54V 机架式配电。资料中心营运商现在评估解决方案的标准是「每瓦每美元的效能」以及 3-5 年折旧免税额週期内的总拥有成本。

竞争格局有利于垂直整合型企业，它们能够统一控制整个晶片和软体堆迭（CUDA、ROCm、Triton、OpenXLA）。虽然通用GPU供应商仍然主导训练领域，但推理领域正日益多元化，转向客製化晶片，因为在这些领域，能源效率和记忆体频宽至关重要。基于FPGA的加速器（例如Xilinx Alveo、Intel Agilex）在低延迟金融、基因组学和讯号处理等领域占有一席之地，在这些领域，可重构性使其较高的单价物有所值。

供应链韧性已成为董事会层面的优先事项。儘管先进封装技术（CoWoS-S、InFO、HDAP）和HBM3/HBM3E记忆体生产集中在台湾和韩国，且美国《晶片法案》和欧盟《晶片法案》的资金正在推动地域多元化，但产能的大幅提升预计仍需24-36个月。

对于企业架构师和采购团队而言，加速器选择目前依赖总体拥有成本 (TCO) 模型，该模型考虑了实例利用率、软体生态系统锁定、电力/冷却基础设施成本以及预期使用寿命。虽然云端市场已基本实现了训练的商品化，但推理仍然高度分散，存在本地定制晶片、云端 GPU 实例和边缘优化硬体等多种选择。

整体而言，加速卡具有显着的架构优势：它们是经济高效地扩展现代人工智慧/机器学习工作负载的唯一可行方案，这得益于生成式人工智慧、云端迁移和科学运算等长期发展趋势，以及架构复杂性不断扩大的领先者和追随者之间的差距。那些掌控效能最高节点、最深厚的软体生态系统和最高效客製化晶片的公司，可望在这个关键的运算基础设施领域实现持续30-7.97%的复合年增长率和超过50%的营运利润率。

本报告的主要优势：

  • 深入分析：取得以客户群、政府政策和社会经济因素、消费者偏好、垂直产业和其他细分市场为重点的深入市场洞察，涵盖主要地区和新兴地区。
  • 竞争格局：了解主要企业采取的策略倡议，并了解透过正确的策略打入市场的潜力。
  • 市场驱动因素与未来趋势：探索动态因素和关键市场趋势，以及它们将如何塑造未来的市场发展。
  • 可执行的建议：利用洞察力为策略决策提供讯息，从而在动态环境中开拓新的业务管道和收入来源。
  • 受众范围广：对新兴企业、研究机构、顾问公司、中小企业和大型企业都有益处且经济高效。

它是用来做什么的？

产业与市场洞察、商业机会评估、产品需求预测、打入市场策略、地理扩张、资本投资决策、法律规范及其影响、新产品开发、竞争影响

分析范围

  • 历史资料（2021-2025 年）和预测资料（2026-2031 年）
  • 成长机会、挑战、供应链前景、法规结构、客户行为和趋势分析
  • 竞争对手定位、策略和市场占有率分析
  • 按业务板块和地区（国家）分類的收入成长和预测分析
  • 公司概况（策略、产品、财务资讯、关键趋势等）

目录

第一章执行摘要

第二章市场概述

  • 市场概览
  • 市场定义
  • 分析范围
  • 市场区隔

第三章 商业情境

  • 市场驱动因素
  • 市场限制
  • 市场机会
  • 波特五力分析
  • 产业价值链分析
  • 政策和法规
  • 策略建议

第四章 技术展望

第五章 加速卡片市场（按类型划分）

  • 介绍
  • 高效能运算加速器
  • 云加速器

第六章 加速卡片市场（依应用领域划分）

  • 介绍
  • 用于深度学习训练
  • 公共云端介面
  • 企业介面

第七章 依处理器类型分類的加速卡市场

  • 介绍
  • CPU（中央处理器）
  • GPU（影像处理单元）
  • FPGA（现场可程式化闸阵列）
  • ASIC（专用积体电路）

第八章 各地区的加速卡市场

  • 介绍
  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 南美洲
    • 巴西
    • 阿根廷
    • 其他的
  • 欧洲
    • 德国
    • 法国
    • 英国
    • 西班牙
    • 其他的
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 其他的
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 印尼
    • 泰国
    • 其他的

第九章：竞争格局与分析

  • 主要企业和策略分析
  • 市占率分析
  • 企业合併、协议、商业合作
  • 竞争对手仪錶板

第十章：公司简介

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

第十一章附录

  • 货币
  • 先决条件
  • 基准年和预测年时间表
  • 相关人员的主要收益
  • 分析方法
  • 简称
简介目录
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