首頁 > 市場調查報告書 > 製藥

精神疾病

市場調查報告書

商品編碼

1972152

2025-2027年人工智慧運算與資料中心領域的顛覆性趨勢

Disruptive Trends in AI Computing and Data Centers, 2025-2027

出版日期: 2026年01月28日 | 出版商:

Frost & Sullivan | 英文 69 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

分散式人工智慧基礎設施、新型經營模式和ESG舉措推動未來成長潛力

顛覆性人工智慧模式正日益挑戰人們對「傳統」雲端資料中心的固有認知。隨著訓練叢集規模擴大到數千個加速器，耗電量高達數十兆瓦，傳統的以CPU為中心、採用風冷的單體伺服器模式已永續。本研究探討了人工智慧基礎設施分拆這一大趨勢，即從傳統的伺服器機箱轉向由加速器、記憶體、儲存、冷卻和電力等元件組成的互聯叢集。我們分析了運算密度、記憶體和I/O瓶頸的日益嚴峻，以及碳排放和水資源限制如何迫使營運商重新思考資料中心的基本設計，包括從傳統伺服器轉向人工智慧專用叢集和可組合叢集架構。未來三到五年，這些變革預計將在決定人工智慧如何在滿足更嚴格的永續性和監管標準的同時，實現經濟高效且可靠的擴展方面發揮關鍵作用。

本報告涵蓋技術發展及其對應用和經營模式的影響，包括以下模組：

人工智慧基礎設施解耦—大趨勢概述
技術概述、架構和分類方案
人工智慧基礎設施的轉型主題
五大主題：
- 異質與專用人工智慧加速器
- 記憶體分散式高頻寬架構
- 冷卻和電源作為核心設計變量
- AI原生編配與自主資料中心
- 永續性、位置和電力系統整合
技術進步應用案例
新興經營模式與實施模式
人工智慧資料中心區域發展趨勢
戰略機會與未來展望

案例研究 1：基於 CXL 的 LLM 訓練單元記憶體分配
案例研究 2：針對人工智慧/高效能運算 (HPC) 和資料庫工作負載的運算儲存磁碟機和資料編配
案例研究3：低碳人工智慧區域及區域供熱
案例研究 4：能源效率和氣候限制下的主權百億億百萬兆級人工智慧

生態系統：由人工智慧運算和資料中心驅動的新型經營模式

一種新的經營模式：加速器艙即服務
新興經營模式：冷氣即服務
新興經營模式：遙測和數據驅動的貨幣化

生態系統：人工智慧運算與資料中心的區域趨勢

人工智慧運算和資料中心的區域發展趨勢

成長來源：趨勢吸引力分析

趨勢吸引力分析

成長機會分析

趨勢、機會、影響分析與確定性分析
趨勢機會革新指數
趨勢分析吸引力評分
趨勢成長指數
成長吸引力評分
BEETS如何影響人工智慧運算與資料中心

成長機會領域

成長機會 1：人工智慧單元和組合式基礎架構園區
成長機會 2：面向人工智慧資料中心的液冷與熱回收平台
成長機會 3：碳感知編配與遙測平台

成長機會分析：成長的關鍵成功因素

成長的關鍵成功因素
結論

附錄

我們的大趨勢宇宙

下一步

成長機會帶來的益處和影響
下一步
圖表清單
免責聲明
人工智慧基礎設施解耦—大趨勢概述

技術概述、架構和分類方案

人工智慧基礎設施的變革性主題

五個詳細主題：
異質與專用人工智慧加速器
記憶體分散式高頻寬架構
冷卻和功率是核心設計變量
AI原生編配與自主資料中心
永續性、位置和電力系統整合

技術進步應用案例

新興經營模式和發展模式

人工智慧資料中心區域發展趨勢

戰略機會與未來展望

簡介目錄

Product Code: DB77-36

AI Infrastructure Unbundling, Emerging Business Models, and ESG Commitments Driving Future Growth Potential

Disruptive AI models are increasingly challenging traditional assumptions about "classic" cloud data centers. As training clusters scale up to thousands of accelerators and consume tens of megawatts of power, the conventional CPU-centric, air-cooled, monolithic server model is no longer sustainable. This research study explores the megatrend of AI infrastructure unbundling, which signifies a shift from traditional server boxes to fabric-connected pools of accelerators, memory, storage, cooling, and power. The study analyzes how growing compute density, memory, and I/O bottlenecks, and constraints related to carbon and water are compelling operators to rethink the fundamental design of data centers. This includes moving from traditional servers to AI pods and composable cluster fabrics. Over the next 3 to 5 years, these disruptions are expected to play a critical role in determining how effectively AI can be scaled in an economical and reliable manner while adhering to stricter sustainability and regulatory standards.

The report, covering technological developments and their impact on deployment and business models, includes the following modules:

AI Infrastructure Unbundling - Megatrend Overview
Technology Overview, Architecture, and Taxonomy
Transformational Themes in AI Infrastructure
Five Deep-Dive Themes:
- Heterogeneous and specialized AI accelerators
- Memory disaggregation and high-bandwidth fabrics
- Cooling and power as core design variables
- AI-native orchestration and autonomic data centers
- Sustainability, siting, and grid integration
Technological Advancement Use Cases
Emerging Business and Deployment Models
Regional Trends in AI-Centric Data Centers
Strategic Opportunities and Future Outlook

Research Scope

Scope of Analysis

Transformational Growth AI Computing and Data Centers

Why is it Increasingly Difficult to Grow?
The Strategic Imperative 8-TM
Our Megatrend Universe-Overview
Our Megatrend Universe-AI Computing and Data Centers
Key Findings

Ecosystem: AI Computing and Data Centers

AI Infrastructure Unbundling: From Servers to Fabric-Connected Pools
Structural Constraints Forcing AI Infrastructure Redesign
AI Pods as the New Unit of Design: Architecture and Strategic Impact

Technology Landscape & Innovation Drivers

AI Data Center Evolution and the AI Compute Stack
Taxonomy of AI-Centric Data Center Architectures: Compute and Resource Coupling
Taxonomy of AI-Centric Data Center Architectures: Deployment, Power, and Cooling

Transformational Themes in AI Infrastructure: Key Megatrends and Sub-Trends

Theme 1: Heterogeneous and Specialized AI Accelerators
Theme 2: Memory Disaggregation and High-Bandwidth Fabrics
Theme 3: Cooling and Power as Core Design Variables
Theme 4: AI-Native Orchestration and Autonomic Data Centers
Theme 5: Sustainability, Siting, and Grid Integration

Companies to Action (C2A)

Case Study 1: CXL-based Memory Disaggregation for LLM Training Pods
Case Study 2: Computational Storage Drives and Data Orchestration for AI/High Performance Computing (HPC) and Database Workloads
Case Study 3: Low-Carbon AI Region with District-Heating Heat Reuse
Case Study 4: Sovereign Exascale AI Under Energy-Efficiency and Climate Constraints

Ecosystem: Emerging Business Models Driven by AI Computing & Data Centers

Emerging Business Model: Accelerator Pods-as-a-Service
Emerging Business Model: Cooling-as-a-Service
Emerging Business Model: Telemetry and Data-Driven Monetization

Ecosystem: Regional Trends for AI Computing & Data Centers

Regional Trends in AI Computing and Data Centers

Growth Generator: Trend Attractiveness Analysis

Trend Attractiveness Analysis

Growth Opportunity Analysis

Trend Opportunity Impact and Certainty Analysis
Trend Opportunity Disruption Index
Trend Disruption Attractiveness Score
Trend Opportunity Growth Index
Growth Attractiveness Score
BEETS Implications for AI Computing and Data Centers

Growth Opportunity Universe

Growth Opportunity 1: AI Pods & Composable Infrastructure Campuses
Growth Opportunity 2: Liquid First Cooling & Heat Reuse Platforms for AI Data Centers
Growth Opportunity 3: Carbon Aware Orchestration & Telemetry Platform