市場調查報告書

雲端及企業資料中心硬體設備 - 伺服器,工作站,卡片,儲存,網路基礎設施的人工智能 (AI):全球市場的分析與預測

Artificial Intelligence in Cloud and Enterprise Data Center Hardware - Servers, Workstations, Cards, Storage, and Networking Infrastructure: Global Market Analysis and Forecasts

出版商 Tractica 商品編碼 914179
出版日期 內容資訊 英文 91 Pages; 59 Tables, Charts & Figures
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雲端及企業資料中心硬體設備 - 伺服器,工作站,卡片,儲存,網路基礎設施的人工智能 (AI):全球市場的分析與預測 Artificial Intelligence in Cloud and Enterprise Data Center Hardware - Servers, Workstations, Cards, Storage, and Networking Infrastructure: Global Market Analysis and Forecasts
出版日期: 2019年10月17日內容資訊: 英文 91 Pages; 59 Tables, Charts & Figures
簡介

本報告提供雲端、企業資料中心中,尤其是電腦,儲存,及網路功能等促進AI基礎設施必要條件的商務、消費者及政府的AI應用的相關調查,市場,生態系統,供應商及技術變化的特性,各地區、功能、晶片組、傳輸模式,及垂直產業分類的基礎設施硬體設備支出預測。

第1章 摘要整理

第2章 市場課題

  • 簡介
  • 定義
  • 推動市場要素
  • 市場障礙
  • 生態系統的問題
  • 超大規模AI的工作負載
  • 企業AI的工作負載

第3章 技術課題

  • 技術趨勢
  • 矽架構 (CPU,GPU,ASIC,FPGA,自訂設計)
  • 運算
  • 記憶體
  • 儲存
  • 網路
  • 傳輸模式:IaaS,PaaS,SaaS
  • 軟體定義資料中心
  • 未來

第4章 主要企業

  • 供應商
  • 白牌供應商
  • 雲端服務供應商

第5章 市場預測

  • 調查範圍、手法
  • AI雲端、企業資料中心硬體設備
  • AI雲端、企業資料中心硬體設備:各地區
  • AI雲端、企業資料中心硬體設備:各功能
  • AI雲端、企業資料中心硬體設備:各電腦類別
  • AI雲端資料中心硬體設備:各傳輸模式
  • AI雲端、企業資料中心硬體設備:各垂直產業
  • 結論、建議

第6章 企業名錄

第7章 縮寫、簡稱清單

第8章 目錄

第9章 圖表

第10章 調查範圍,資訊來源,調查手法,註記

目錄
Product Code: CEDC-19

The first movers in artificial intelligence (AI) have been the hyperscaler operators. This is partly because their businesses had progressed to the point where they needed AI. Google needed AI to optimize web searches; Amazon to do customization of its online retail offerings; and Facebook to enhance its activity feed, photo, and social media applications. The other reason is that the hyperscalers are the ones with the deep pockets to fund the high costs of research in AI. These companies are now attempting to democratize AI technology and make it pervasive.

Data center infrastructure, specifically computing, memory, storage, and networking, is in the process of going through a reboot to support AI. Though AI represents just a small portion of a cloud data center's workload and an even smaller portion of an enterprise's workload, it drives a different type of application profile and thus requires different architectures and components. Advances in technology have played a major part in enabling AI expansion and market penetration. In turn, AI applications are driving the development of new silicon and system architectures, storage and networking options, and delivery models. Meanwhile, Tractica's research indicates that enterprises are not abandoning on-premise computing. While the hyperscalers have been driving AI implementation in the cloud, there is corresponding demand for on-premise and colocated solutions from early adopter enterprises.

This Tractica report examines the AI applications in business, consumer, and government that are driving requirements in AI infrastructure, especially the compute, storage, and networking functions in cloud and enterprise data centers. The report also catalogs the changing nature of the market, ecosystem, vendors, and technologies, including the underlying semiconductors powering the next generation in AI. Market forecasts include infrastructure hardware spend from 2018 to 2025 segmented by region, function, chipset, delivery model, and enterprise vertical.

Key Market Forecasts

  • Cloud and Enterprise Data Center Hardware Revenue for AI by Vertical, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Segment, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Region, World Markets: 2018-2025
  • AI Initiatives, Industry vs. Research Focus, U.S., China, and Europe: 2018
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Function, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Compute Category, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Vertical, World Markets: 2018-2025

Verticals

  • Banking & Financial
  • Retail
  • Automotive & Transportation
  • Telecom & Broadband
  • Healthcare
  • Manufacturing
  • Consumer Packaged Goods
  • Government
  • Travel & Tourism
  • Education
  • Other

Functions and Delivery

  • Models
  • Computing
  • Storage
  • Networking
  • Infrastructure as a service (IaaS)
  • Platform as a service (PaaS)
  • Software as a service (SaaS)

Geographies

  • North America
  • Europe
  • Asia Pacific
  • Latin America
  • Middle East and Africa

Table of Contents

1. Executive Summary

  • 1.1. Introduction
    • 1.1.1. Definitions
  • 1.2. Market Drivers
  • 1.3. Market Barriers
  • 1.4. Technology Issues
  • 1.5. Market Ecosystem
  • 1.6. Market Forecasts

2. Market Issues

  • 2.1. Introduction
    • 2.1.1. First Movers and Early Innovators
    • 2.1.2. Data Center Infrastructure
  • 2.2. Definitions
    • 2.2.1. Cloud and Enterprise Data Center
    • 2.2.2. Public, Private, and Hybrid Clouds
  • 2.3. Market Drivers
    • 2.3.1. Increasing Interest in AI from Cloud Hyperscale Operators
    • 2.3.2. Increasing Interest in AI from Colocation and Tier 2 Operators
    • 2.3.3. Increasing Interest in AI from Enterprises
    • 2.3.4. Increase in Diversity and Complexity of AI Applications and Models
    • 2.3.5. Interest in AI from Global Governments
    • 2.3.6. Growth in AI Startups, Investments, Education, and Jobs
  • 2.4. Market Barriers
    • 2.4.1. Decentralized AI at the Edge
    • 2.4.2. Data Center Costs
    • 2.4.3. Lack of Robust Enterprise Architectures and Data Frameworks
    • 2.4.4. Issues of Privacy
    • 2.4.5. Shortcomings of AI
  • 2.5. Ecosystem Questions
    • 2.5.1. U.S.-China Trade War
    • 2.5.2. The Rise of White Box Vendors
    • 2.5.3. Hyperscalers and DIY Silicon
    • 2.5.4. Enterprises - Should They Implement in Cloud or On-Premise?
  • 2.6. Hyperscaler AI Workloads
  • 2.7. Enterprise AI Workloads

3. Technology Issues

  • 3.1. Technology Trends
  • 3.2. Silicon Architectures (CPU, GPU, ASIC, FPGA, Custom Design)
    • 3.2.1. CPU
    • 3.2.2. GPU
    • 3.2.3. FPGA
    • 3.2.4. ASIC
    • 3.2.5. Custom Design
  • 3.3. Computing
  • 3.4. Memory
  • 3.5. Storage
  • 3.6. Networking
    • 3.6.1. 400 GbE Optical Connections
    • 3.6.2. Smart Network Interface Cards (SmartNICs)
    • 3.6.3. Intent-Based Networking Systems (IBNS)
  • 3.7. Delivery Models: IaaS, PaaS, SaaS
    • 3.7.1. Infrastructure as a Service (IaaS)
    • 3.7.2. Platform as a Service (PaaS)
    • 3.7.3. Software as a Service (SaaS)
    • 3.7.4. Choose the Service
  • 3.8. Software-Defined Data Center
  • 3.9. The Future

4. Key Industry Players

  • 4.1. Vendors
    • 4.1.1. Cisco
    • 4.1.2. Dell
    • 4.1.3. HPE
    • 4.1.4. Huawei
    • 4.1.5. IBM
    • 4.1.6. Inspur
    • 4.1.7. Lenovo
    • 4.1.8. NetApp
  • 4.2. White Box Vendors
    • 4.2.1. ASUSTeK
    • 4.2.2. Compal
    • 4.2.3. Honhai/Foxconn
    • 4.2.4. Inventec
    • 4.2.5. Pegatron
    • 4.2.6. Quanta
    • 4.2.7. Wistron
  • 4.3. Cloud Service Providers
    • 4.3.1. Alibaba
    • 4.3.2. Amazon
    • 4.3.3. Baidu
    • 4.3.4. Data Foundry
    • 4.3.5. Equinix
    • 4.3.6. Flexential
    • 4.3.7. Google
    • 4.3.8. Microsoft
    • 4.3.9. Tencent

5. Market Forecasts

  • 5.1. Scope and Methodology
    • 5.1.1. Hardware Infrastructure and Additional Data
    • 5.1.2. Top-Down Approach
    • 5.1.3. Definitions
    • 5.1.4. Regions
    • 5.1.5. Beyond 2025
  • 5.2. Cloud and Enterprise Data Center Hardware for AI
  • 5.3. Cloud and Enterprise Data Center Hardware for AI by Region
  • 5.4. Cloud and Enterprise Data Center Hardware for AI by Function
  • 5.5. Cloud and Enterprise Data Center Hardware for AI by Compute Category
  • 5.6. Cloud Data Center Hardware for AI by Delivery Model
  • 5.7. Cloud and Enterprise Data Center Hardware for AI by Vertical
    • 5.7.1. Banking and Financial
    • 5.7.2. Retail
    • 5.7.3. Automotive and Transportation
    • 5.7.4. Telecom and Broadband and Energy
    • 5.7.5. Healthcare
    • 5.7.6. Manufacturing
    • 5.7.7. Consumer Packaged Goods
    • 5.7.8. Government
    • 5.7.9. Travel and Tourism
    • 5.7.10. Education
    • 5.7.11. Others
  • 5.8. Conclusions and Recommendations

6. Company Directory

7. Acronym and Abbreviation List

8. Table of Contents

9. Table of Charts and Figures

10. Scope of Study, Sources and Methodology, Notes

Tables

  • Cloud and Enterprise Data Center Hardware Revenue for AI by Segment, World Markets: 2018-2025
  • Year-over-Year Growth, Cloud and Enterprise Data Center Hardware Revenue for AI, World Markets: 2019-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Region, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Function, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Compute Category, World Markets: 2018-2025
  • Cloud Data Center Hardware Revenue for AI by Delivery Model, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Vertical, World Markets: 2018-2025
  • Growth of Cloud Data Centers, Global vs. U.S.: 2019
  • Paperspace's GPU-Powered Virtual Machine
  • Power Density in a Data Center: 2009 vs. 2019
  • Server Market Share: End 2018
  • Baidu Kunlun Processor Features
  • Type of AI Workloads Running at the Cloud Data Center: 2018 and 2025
  • Enterprise AI Workloads Segmented by Location Where They Run: 2018 and 2025
  • Silicon Alternatives for AI
  • Hyperscaler IaaS, PaaS, and SaaS Solutions

Charts

  • Cloud and Enterprise Data Center Hardware Revenue for AI by Segment, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Vertical, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Segment, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Region, World Markets: 2018-2025
  • AI Initiatives, Industry vs. Research Focus, U.S., China, and Europe: 2018
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Function, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Compute Category, World Markets: 2018-2025
  • Cloud Data Center Hardware Revenue for AI by Delivery Model, World Markets: 2018-2025
  • Cloud and Enterprise Data Center Hardware Revenue for AI by Vertical, World Markets: 2018-2025

Figures

  • Fourth Industrial Revolution
  • Data from Autonomous Vehicles
  • R&D Investments: 1Q 2018 and 2Q 2018
  • Adoption of AI
  • Public Cloud, Private Cloud, and On-Premise
  • PUE Improvement by Google
  • Capabilities of AI
  • AI Startups vs. All Startups
  • AI Skill Requirements in Job Postings
  • Edge vs. Cloud Computing
  • Data Center Electricity Use (Billions of kWh/Year): 2006-2020
  • Google's TPU on a Printed Circuit Board and Inside a Data Center
  • Silicon Alternatives for AI
  • NVIDIA's T4 GPU
  • Amazon's FPGA Acceleration
  • Qualcomm Cloud AI 100
  • Google's TPUv2
  • AMD's HBM
  • NVIDIA DGX-1 with Pure Storage
  • Ethernet Evolution
  • Cloud Computing Delivery Models
  • Cloud Computing Delivery Models
  • SDDC Architecture
  • Cisco Rack Server
  • Dell EMC PowerMax All-Flash Enterprise Data Storage
  • IBM Watson Studio
  • Inspur's AI Offering
  • ThinkStation 920 for AI
  • HGX-1 for AI Acceleration
  • QCT's Platforms for Machine Learning
  • Roadmap of AI
  • Primary Components Inside a Data Center
  • Hyperconverged Infrastructure (HCI)
  • IaaS, PaaS, SaaS Architectures
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