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市場調查報告書
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2029982

全球直連式人工智慧儲存系統市場：按容量、類型、應用和最終用戶分類－市場規模、產業動態、機會分析和預測（2026-2035 年）

Global Direct Attached AI Storage System Market: By Capacity, Type, Application, End User - Market Size, Industry Dynamics, Opportunity Analysis and Forecast for 2026-2035
出版日期: | 出版商: Astute Analytica | 英文 280 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

直連式人工智慧儲存系統市場正在快速擴張，預計2025年市場規模將達到121.9億美元，到2035年將達到約501.8億美元。這意味著在2026年至2035年的預測期內，複合年成長率將達到15.20%。這種強勁的成長勢頭表明，企業、超大規模和研究環境中對能夠支援日益複雜的人工智慧（AI）工作負載的高效能儲存架構的需求正在加速成長。

推動這項擴展的主要動力在於消除高密度人工智慧運算環境中的效能瓶頸。隨著企業大規模模型訓練和推理工作負載的擴展，確保計算單元的資料持續供應至關重要。尤其重要的是，防止「GPU飢餓」（即由於資料吞吐量不足導致處理單元閒置）已成為現代基礎設施規劃的核心設計重點。

顯著的市場趨勢

直連式AI儲存廠商生態系統在頂層實現了高度整合，主導集中在那些成功實現硬體設計、製造和供應鏈營運深度垂直整合的OEM廠商手中。這些領先廠商也與NVIDIA和AMD等主要G​​PU生態系統參與者建立了牢固的策略夥伴關係，從而能夠為需要極高頻寬和低延遲儲存效能的AI工作負載提供精細的配置。

在競爭激烈的市場環境中，Supermicro憑藉其模組化、模組化的架構贏得了壓倒性的市場支持，這種架構能夠快速客製化針對人工智慧最佳化的伺服器和儲存配置。同樣，戴爾科技集團也憑藉PowerEdge XE系列產品鞏固了其市場地位。此系列產品專為人工智慧工作負載而設計，並整合了高密度直連式NVMe背板，以支援高吞吐量資料管道。

在頂級供應商之下，聯想和Cisco等二級供應商，以及廣達電腦和維維等區域性ODM廠商，在生態系統中扮演著至關重要的支撐角色。這些公司為擴大生產規模、最佳化成本和提升供應鏈柔軟性做出了重大貢獻，確保超大規模和企業級市場對AI儲存基礎設備的需求都能得到滿足。

主要成長要素

直接連接式人工智慧儲存系統市場的成長主要受非結構化資料呈指數級成長以及大規模人工智慧模型工作負載快速擴張的驅動。現代人工智慧應用，尤其是那些以基礎模型和多模態系統為中心的應用，如今每次訓練或推理作業通常需要數十至數百Terabyte的資料。資料Terabyte的激增促使儲存架構重新設計，因為傳統系統難以滿足下一代運算環境對效能和延遲的要求。

新機會的趨勢

在人工智慧儲存市場，技術主導地位越來越取決於供應商能夠多快地採用、整合和商業化下一代互連標準，這些標準從根本上改變了資料傳輸和記憶體架構。特別是，Compute Express Link (CXL) 2.0 和 3.0 代表了過去十年中直連式人工智慧儲存生態系統中最具變革性的進步之一，它將系統設計從孤立的記憶體孤島轉變為更統一、可組合的基礎架構模型。

最佳化障礙

儘管市場需求爆炸性成長，但直連式人工智慧儲存系統市場目前正面臨嚴重的營運摩擦，這些摩擦正日益影響供應商的策略和盈利。產業報告指出，持續的供應鏈瓶頸正在延長生產和交付前置作業時間，從而延遲高價值企業級和超大規模訂單的交付。在人工智慧基礎設施部署計畫與運算擴展週期緊密相關的環境中，這些限制的影響尤其顯著，因為儲存可用性是整個系統部署的關鍵促進因素。

目錄

第1章執行摘要：全球直連式人工智慧儲存系統市場

第2章：調查方法與研究框架

  • 研究目標
  • 產品概述
  • 市場區隔
  • 定性研究
    • 一手和二手資訊
  • 量化研究
    • 一手和二手資訊
  • 主要調查受訪者組成：按地區分類
  • 本研究的前提
  • 市場規模估算
  • 數據三角測量

第3章：全球直連式人工智慧儲存系統市場概述

  • 產業價值鏈分析
  • 產業展望
    • 人工智慧基礎設施和儲存架構的演進
    • 從網路附加儲存遷移到直接附加存儲
    • GPU、NVMe 和低延遲架構的作用
    • 案例分析
    • 法規概述
  • PESTLE分析
  • 波特五力分析
  • 市場成長及前景
    • 2020-2035年市場收入估算與預測
    • 價格趨勢分析

第4章：全球直連式人工智慧儲存系統市場分析

  • 競爭對手儀錶板
    • 市場集中度
    • 企業市場占有率分析，2025 年
    • 競爭對手分析與基準測試

第5章：全球直連式人工智慧儲存系統市場分析

  • 市場動態和趨勢
    • 成長要素
    • 抑制因子
    • 機會
    • 主要趨勢
  • 市場規模及預測，2020-2035年
    • 按產能
      • 關鍵見解
        • 小於5TB
        • 5TB～20TB
        • 20TB～50TB
        • 超過50TB
    • 按類型
      • 關鍵見解
        • 硬碟機
        • 固態硬碟
        • 混合儲存
        • 網路附加儲存
    • 透過使用
      • 關鍵見解
        • 資料分析
        • 機器學習
        • 人工智慧
        • 深度學習
        • 巨量資料
    • 最終用戶
      • 關鍵見解
        • 小型企業
        • 大公司
        • 政府
    • 按地區
      • 關鍵見解
        • 北美洲
          • 美國
          • 加拿大
          • 墨西哥
        • 歐洲
          • 西歐
            • 英國
            • 德國
            • 法國
            • 義大利
            • 西班牙
            • 其他西歐國家
          • 東歐
            • 波蘭
            • 俄羅斯
            • 其他東歐國家
        • 亞太地區
          • 中國
          • 印度
          • 日本
          • 韓國
          • 澳洲和紐西蘭
          • ASEAN
            • 印尼
            • 馬來西亞
            • 泰國
            • 新加坡
            • 其他東南亞國協
          • 其他亞太國家
        • 中東和非洲
          • UAE
          • 沙烏地阿拉伯
          • 南非
          • 其他中東和非洲國家
        • 南美洲
          • 阿根廷
          • 巴西
          • 其他南美國家

第6章：北美市場分析

第7章：歐洲市場分析

第8章：亞太市場分析

第9章：中東和非洲市場分析

第10章：南美市場分析

第11章：公司簡介

  • HP Enterprise
  • Seagate Technology
  • Western Digital
  • Dell Technologies
  • NetApp
  • Toshiba
  • Samsung Electronics
  • IBM
  • Micron Technology
  • Other Prominent Players

第12章附錄

簡介目錄
Product Code: AA04261756

The direct-attached AI storage system market is undergoing rapid expansion, with its value estimated at USD 12.19 billion in 2025 and projected to reach approximately USD 50.18 billion by 2035, reflecting a compound annual growth rate (CAGR) of 15.20% over the 2026-2035 forecast period. This strong growth trajectory highlights the accelerating demand for high-performance storage architectures that can support increasingly complex artificial intelligence workloads across enterprise, hyperscale, and research environments.

A primary factor driving this expansion is the critical requirement to eliminate performance bottlenecks in high-density AI computing environments. As organizations scale large-scale model training and inference workloads, ensuring continuous data availability to compute units has become essential. In particular, preventing GPU starvation-where processing units remain idle due to insufficient data throughput-has emerged as a central design priority in modern infrastructure planning.

Noteworthy Market Developments

The vendor ecosystem for Direct Attached AI Storage is highly consolidated at the top, with leadership concentrated among OEMs that have successfully achieved deep vertical integration across hardware design, manufacturing, and supply chain operations. These leading vendors have also established strong strategic partnerships with major GPU ecosystem players such as NVIDIA and AMD, enabling tightly optimized configurations for AI workloads that require extreme bandwidth and low-latency storage performance.

Within this competitive structure, Supermicro has gained disproportionate market traction, largely due to its modular building-block architecture that allows rapid customization of AI-optimized server and storage configurations. Similarly, Dell Technologies has strengthened its position through its PowerEdge XE series, which is specifically engineered for AI workloads and integrates dense, direct-attached NVMe backplanes to support high-throughput data pipelines.

Below the top tier, Tier 2 vendors such as Lenovo, Cisco Systems, and regional ODMs like Quanta Computer and Wiwynn play an essential supporting role in the ecosystem. These companies contribute significantly to manufacturing scale, cost optimization, and supply chain flexibility, ensuring that demand for AI storage infrastructure can be met across both hyperscale and enterprise segments.

Core Growth Drivers

The growth of the direct-attached AI storage system market is being strongly driven by the exponential rise in unstructured data generation and the rapid scaling of large-model AI workloads. Modern artificial intelligence applications, particularly those built around foundation models and multimodal systems, now routinely require tens to hundreds of terabytes of data per training or inference job. This surge in data intensity is reshaping storage architectures, as traditional systems struggle to meet the performance and latency demands of next-generation compute environments.

Emerging Opportunity Trends

The technological moat in the AI storage market is increasingly defined by how quickly vendors can adopt, integrate, and commercialize next-generation interconnect standards that fundamentally reshape data movement and memory architecture. In particular, Compute Express Link (CXL) 2.0 and 3.0 represent some of the most transformative developments in the direct-attached AI storage ecosystem in over a decade, as they shift system design away from isolated memory silos toward a more unified and composable infrastructure model.

Barriers to Optimization

Despite explosive demand, the direct-attached AI storage system market is currently facing significant operational friction that is increasingly shaping vendor strategy and profitability. Industry intelligence indicates that persistent supply chain bottlenecks are extending production and delivery lead times, creating delays in fulfilling high-value enterprise and hyperscale orders. These constraints are particularly impactful in environments where AI infrastructure deployment timelines are tightly coupled with compute expansion cycles, making storage availability a critical pacing factor for overall system rollout.

Detailed Market Segmentation

By storage medium, the NVMe SSD segment held the largest market share of approximately 62.36% in 2025, reflecting its critical role in meeting the extreme performance requirements of modern AI-driven computing environments. This dominance is primarily driven by the need for ultra-low latency, exceptionally high throughput, and scalable storage architectures capable of sustaining continuous data flow to high-performance compute clusters.

By application, the LLM training and fine-tuning segment accounted for a dominant market share of approximately 65.94% in 2025, reflecting the rapid expansion of large-scale generative AI workloads across enterprise, research, and hyperscale computing environments. This leadership position is primarily driven by the computational intensity and data-heavy nature of training Large Language Model systems with billions to trillions of parameters. As organizations increasingly invest in foundation models and domain-specific AI systems, the demand for high-throughput, low-latency storage infrastructure has surged significantly.

By capacity range, the above 5 to 20 TB per node segment held a dominant share of approximately 43% in 2025, reflecting its optimal balance between performance, scalability, and cost efficiency in modern storage-intensive computing environments. This capacity tier has emerged as the preferred configuration for a wide range of enterprise and high-performance workloads, particularly in data-heavy applications such as artificial intelligence training, analytics processing, and large-scale virtualization.

Segment Breakdown

By Capacity

  • Below 5TB
  • 5TB to 20TB
  • 20TB to 50TB
  • Above 50TB

By Type

  • Hard Disk Drive
  • Solid State Drive
  • Hybrid Storage
  • Network Attached Storage

By Application

  • Data Analytics
  • Machine Learning
  • Artificial Intelligence
  • Deep Learning
  • Big Data

By End User

  • Small and Medium Enterprises
  • Large Enterprises
  • Government

By Region

  • North America
  • The U.S.
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia & New Zealand
  • South Korea
  • ASEAN
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • Saudi Arabia
  • South Africa
  • UAE
  • Rest of MEA
  • South America
  • Argentina
  • Brazil
  • Rest of South America

Geography Breakdown

  • North America accounted for the largest market share, holding approximately 36.54% in 2025, reflecting its dominant position in the global ecosystem for high-performance computing and AI-driven infrastructure deployment. This leadership is strongly reinforced by the region's rapid expansion of large-scale GPU cluster installations, with more than 70% of deployments exceeding 100,000 GPUs currently concentrated within North American data centers.
  • Within this landscape, the United States plays a central and defining role as the primary hub for AI infrastructure development and deployment in the direct-attached AI storage system market. The country's leadership is driven by a combination of private sector innovation, hyperscaler investment, and a highly mature digital ecosystem that supports rapid adoption of next-generation compute architectures.
  • This growth trajectory is further strengthened by large-scale federal initiatives aimed at reinforcing domestic semiconductor and advanced manufacturing capabilities. Policies such as the CHIPS and Science Act have created downstream effects that enhance supply chain resilience and encourage localized production of critical hardware components. As a result, the United States has been able to support an exceptionally high concentration of high-performance direct-attached storage (DAS) arrays, enabling the efficient handling of AI workloads at unprecedented scale.

Leading Market Participants

  • HP Enterprise
  • Seagate Technology
  • Western Digital
  • Dell Technologies
  • NetApp
  • Toshiba
  • Samsung Electronics
  • IBM
  • Micron Technology
  • Other Prominent Players

Table of Content

Chapter 1. Executive Summary: Global Direct Attached AI Storage System Market

Chapter 2. Research Methodology & Research Framework

  • 2.1. Research Objective
  • 2.2. Product Overview
  • 2.3. Market Segmentation
  • 2.4. Qualitative Research
    • 2.4.1. Primary & Secondary Sources
  • 2.5. Quantitative Research
    • 2.5.1. Primary & Secondary Sources
  • 2.6. Breakdown of Primary Research Respondents, By Region
  • 2.7. Assumption for Study
  • 2.8. Market Size Estimation
  • 2.9. Data Triangulation

Chapter 3. Global Direct Attached AI Storage System Market Overview

  • 3.1. Industry Value Chain Analysis
    • 3.1.1. Semiconductor & NAND Manufacturers
    • 3.1.2. Storage Hardware OEMs (Servers & DAS Systems)
    • 3.1.3. Controller & Interface Chip Providers
    • 3.1.4. Software-Defined Storage (SDS) Providers
    • 3.1.5. Cloud & Data Center Operators
    • 3.1.6. End Users
  • 3.2. Industry Outlook
    • 3.2.1. Evolution of AI Infrastructure & Storage Architectures
    • 3.2.2. Transition from Network-Attached to Direct-Attached Storage
    • 3.2.3. Role of GPUs, NVMe & Low-Latency Architectures
    • 3.2.4. Case Study Analysis
    • 3.2.5. Regulatory Overview
  • 3.3. PESTLE Analysis
  • 3.4. Porter's Five Forces Analysis
    • 3.4.1. Bargaining Power of Suppliers
    • 3.4.2. Bargaining Power of Buyers
    • 3.4.3. Threat of Substitutes
    • 3.4.4. Threat of New Entrants
    • 3.4.5. Degree of Competition
  • 3.5. Market Growth and Outlook
    • 3.5.1. Market Revenue Estimates and Forecast (US$ Mn), 2020-2035
    • 3.5.2. Price Trend Analysis

Chapter 4. Global Direct Attached AI Storage System Market Analysis

  • 4.1. Competition Dashboard
    • 4.1.1. Market Concentration Rate
    • 4.1.2. Company Market Share Analysis (Value %), 2025
    • 4.1.3. Competitor Mapping & Benchmarking

Chapter 5. Global Direct Attached AI Storage System Market Analysis

  • 5.1. Market Dynamics and Trends
    • 5.1.1. Growth Drivers
    • 5.1.2. Restraints
    • 5.1.3. Opportunity
    • 5.1.4. Key Trends
  • 5.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 5.2.1. By Capacity
      • 5.2.1.1. Key Insights
        • 5.2.1.1.1. Below 5TB
        • 5.2.1.1.2. 5TB to 20TB
        • 5.2.1.1.3. 20TB to 50TB
        • 5.2.1.1.4. Above 50TB
    • 5.2.2. By Type
      • 5.2.2.1. Key Insights
        • 5.2.2.1.1. Hard Disk Drive
        • 5.2.2.1.2. Solid State Drive
        • 5.2.2.1.3. Hybrid Storage
        • 5.2.2.1.4. Network Attached Storage
    • 5.2.3. By Application
      • 5.2.3.1. Key Insights
        • 5.2.3.1.1. Data Analytics
        • 5.2.3.1.2. Machine Learning
        • 5.2.3.1.3. Artificial Intelligence
        • 5.2.3.1.4. Deep Learning
        • 5.2.3.1.5. Big Data
    • 5.2.4. By End User
      • 5.2.4.1. Key Insights
        • 5.2.4.1.1. Small and Medium Enterprises
        • 5.2.4.1.2. Large Enterprises
        • 5.2.4.1.3. Government
    • 5.2.5. By Region
      • 5.2.5.1. Key Insights
        • 5.2.5.1.1. North America
          • 5.2.5.1.1.1. The U.S.
          • 5.2.5.1.1.2. Canada
          • 5.2.5.1.1.3. Mexico
        • 5.2.5.1.2. Europe
          • 5.2.5.1.2.1. Western Europe
            • 5.2.5.1.2.1.1. The UK
            • 5.2.5.1.2.1.2. Germany
            • 5.2.5.1.2.1.3. France
            • 5.2.5.1.2.1.4. Italy
            • 5.2.5.1.2.1.5. Spain
            • 5.2.5.1.2.1.6. Rest of Western Europe
          • 5.2.5.1.2.2. Eastern Europe
            • 5.2.5.1.2.2.1. Poland
            • 5.2.5.1.2.2.2. Russia
            • 5.2.5.1.2.2.3. Rest of Eastern Europe
        • 5.2.5.1.3. Asia Pacific
          • 5.2.5.1.3.1. China
          • 5.2.5.1.3.2. India
          • 5.2.5.1.3.3. Japan
          • 5.2.5.1.3.4. South Korea
          • 5.2.5.1.3.5. Australia & New Zealand
          • 5.2.5.1.3.6. ASEAN
            • 5.2.5.1.3.6.1. Indonesia
            • 5.2.5.1.3.6.2. Malaysia
            • 5.2.5.1.3.6.3. Thailand
            • 5.2.5.1.3.6.4. Singapore
            • 5.2.5.1.3.6.5. Rest of ASEAN
          • 5.2.5.1.3.7. Rest of Asia Pacific
        • 5.2.5.1.4. Middle East & Africa
          • 5.2.5.1.4.1. UAE
          • 5.2.5.1.4.2. Saudi Arabia
          • 5.2.5.1.4.3. South Africa
          • 5.2.5.1.4.4. Rest of MEA
        • 5.2.5.1.5. South America
          • 5.2.5.1.5.1. Argentina
          • 5.2.5.1.5.2. Brazil
          • 5.2.5.1.5.3. Rest of South America

Chapter 6. North America Market Analysis

  • 6.1. Market Dynamics and Trends
    • 6.1.1. Growth Drivers
    • 6.1.2. Restraints
    • 6.1.3. Opportunity
    • 6.1.4. Key Trends
  • 6.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 6.2.1. Key Insights
      • 6.2.1.1. By Capacity
      • 6.2.1.2. By Type
      • 6.2.1.3. By Application
      • 6.2.1.4. By End User
      • 6.2.1.5. By Country

Chapter 7. Europe Market Analysis

  • 7.1. Market Dynamics and Trends
    • 7.1.1. Growth Drivers
    • 7.1.2. Restraints
    • 7.1.3. Opportunity
    • 7.1.4. Key Trends
  • 7.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 7.2.1. Key Insights
      • 7.2.1.1. By Capacity
      • 7.2.1.2. By Type
      • 7.2.1.3. By Application
      • 7.2.1.4. By End User
      • 7.2.1.5. By Country

Chapter 8. Asia Pacific Market Analysis

  • 8.1. Market Dynamics and Trends
    • 8.1.1. Growth Drivers
    • 8.1.2. Restraints
    • 8.1.3. Opportunity
    • 8.1.4. Key Trends
  • 8.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 8.2.1. Key Insights
      • 8.2.1.1. By Capacity
      • 8.2.1.2. By Type
      • 8.2.1.3. By Application
      • 8.2.1.4. By End User
      • 8.2.1.5. By Country

Chapter 9. Middle East & Africa Market Analysis

  • 9.1. Market Dynamics and Trends
    • 9.1.1. Growth Drivers
    • 9.1.2. Restraints
    • 9.1.3. Opportunity
    • 9.1.4. Key Trends
  • 9.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 9.2.1. Key Insights
      • 9.2.1.1. By Capacity
      • 9.2.1.2. By Type
      • 9.2.1.3. By Application
      • 9.2.1.4. By End User
      • 9.2.1.5. By Country

Chapter 10. South America Market Analysis

  • 10.1. Market Dynamics and Trends
    • 10.1.1. Growth Drivers
    • 10.1.2. Restraints
    • 10.1.3. Opportunity
    • 10.1.4. Key Trends
  • 10.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 10.2.1. Key Insights
      • 10.2.1.1. By Capacity
      • 10.2.1.2. By Type
      • 10.2.1.3. By Application
      • 10.2.1.4. By End User
      • 10.2.1.5. By Country

Chapter 11. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

  • 11.1. HP Enterprise
  • 11.2. Seagate Technology
  • 11.3. Western Digital
  • 11.4. Dell Technologies
  • 11.5. NetApp
  • 11.6. Toshiba
  • 11.7. Samsung Electronics
  • 11.8. IBM
  • 11.9. Micron Technology
  • 11.10. Other Prominent Players

Chapter 12. Annexure

  • 12.1. List of Secondary Sources
  • 12.2. Key Country Markets- Macro Economic Outlook/Indicators