Artificial Intelligence in Cloud and Enterprise Data Center Hardware

Servers, Workstations, Cards, Storage, and Networking Infrastructure: Global Market Analysis and Forecasts

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Pages: 91
Tables, Charts,
     & Figures:
59
Publication Date: 4Q 2019
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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 Questions Addressed:

  • What is the current state of the AI market and how will it develop over the next decade?
  • What are the key market drivers and barriers and ecosystem trends with cloud and enterprise data center hardware for AI?
  • How will AI be implemented in the cloud, enterprise, or colocation site?
  • What are the key technologies that will affect computing, storage, and networking in the cloud and enterprise data center?
  • Which companies are the key players in the AI market and what are their initiatives and offerings for cloud and enterprise data center hardware?
  • What is the size of the cloud and enterprise hardware infrastructure market to support AI, and what is its trajectory over the next 7 years?

Who Needs This Report?

  • Cloud hyperscaler operators
  • Colocation operators
  • Data center server and workstation vendors
  • Memory and storage vendors
  • Switching, routing, and networking vendors
  • AI semiconductor vendors
  • Enterprises
  • Governments and regulatory bodies
  • Investor community

Table of Contents

  1. Executive Summary
    1.  Introduction
      1. Definitions
    2. Market Drivers
    3. Market Barriers
    4. Technology Issues
    5. Market Ecosystem
  2. Market Issues
    1. Introduction
      1. First Movers and Early Innovators
      2. Data Center Infrastructure
    2. Definitions
      1. Cloud and Enterprise Data Center
      2. Public, Private, and Hybrid Clouds
    3. Market Drivers
      1. Increasing Interest in AI from Cloud Hyperscale Operators
      2. Increasing Interest in AI from Colocation and Tier 2 Operators
      3. Increasing Interest in AI from Enterprises
      4. Increase in Diversity and Complexity of AI Applications and Models
      5. Interest in AI from Global Governments
      6. Growth in AI Startups, Investments, Education, and Jobs
    4. Market Barriers
      1. Decentralized AI at the Edge
      2. Data Center Costs
      3. Lack of Robust Enterprise Architectures and Data Frameworks
      4. Issues of Privacy
      5. Shortcomings of AI
    5. Ecosystem Questions
      1. U.S.-China Trade War
      2. The Rise of White Box Vendors
      3. Hyperscalers and DIY Silicon
      4. Enterprises – Should They Implement in Cloud or On-Premise?
    6. Hyperscaler AI Workloads
    7. Enterprise AI Workloads
  3. Technology Issues
    1. Technology Trends
    2. Silicon Architectures (CPU, GPU, ASIC, FPGA, Custom Design)
      1. CPU
      2. GPU
      3. FPGA
      4. ASIC
      5. Custom Design
    3. Computing
    4. Memory
    5. Storage
    6. Networking
      1. 400 GbE Optical Connections
      2. Smart Network Interface Cards (SmartNICs)
      3. Intent-Based Networking Systems (IBNS)
    7. Delivery Models: IaaS, PaaS, SaaS
      1. Infrastructure as a Service (IaaS)
      2. Platform as a Service (PaaS)
      3. Software as a Service (SaaS)
      4. Choose the Service
    8. Software-Defined Data Center
    9. The Future
  4. Key Industry Players
    1. Vendors
      1. Cisco
      2. Dell
      3. HPE
      4. Huawei
      5. IBM
      6. Inspur
      7. Lenovo
      8. NetApp
    2. White Box Vendors
      1. ASUSTeK
      2. Compal
      3. Honhai/Foxconn
      4. Inventec
      5. Pegatron
      6. Quanta
      7. Wistron
    3. Cloud Service Providers
      1. Alibaba
      2. Amazon
      3. Baidu
      4. Data Foundry
      5. Equinix
      6. Flexential
      7. Google
      8. Microsoft
      9. Tencent
  5. Market Forecasts
    1. Scope and Methodology
      1. Hardware Infrastructure and Additional Data
      2. Top-Down Approach
      3. Definitions
      4. Regions
      5. Beyond 2025
    2. Cloud and Enterprise Data Center Hardware for AI
    3. Cloud and Enterprise Data Center Hardware for AI by Region
    4. Cloud and Enterprise Data Center Hardware for AI by Function
    5. Cloud and Enterprise Data Center Hardware for AI by Compute Category
    6. Cloud Data Center Hardware for AI by Delivery Model
    7. Cloud and Enterprise Data Center Hardware for AI by Vertical
      1. Banking and Financial
      2. Retail
      3. Automotive and Transportation
      4. Telecom and Broadband and Energy
      5. Healthcare
      6. Manufacturing
      7. Consumer Packaged Goods
      8. Government
      9. Travel and Tourism
      10. Education
      11. Others
    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

List of Charts, Figures, and Tables

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
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