人工智慧硬體設備的全球市場規模:各元件類型，各用途，各終端用戶產業，各地區，範圍及預測

人工智慧硬體設備的市場規模與預測

人工智慧硬體設備市場規模，被預測2023年541億美元被估價，從2024年開始2030年的在預測期間內38.73 %的年複合成長率增長，2030年達到4,741億美元。

人工智慧硬體設備的全球市場促進因素

人工智慧硬體設備市場推動市場要素，有受到各式各樣的因素的影響的可能性。

AI硬體設備的需求，由於醫療保健，汽車，金融，零售，製造等，多的產業AI廣泛地所使用的事變成要素。AI，以自動化，資料分析，模式認識等的目的各產業被利用，為了效率性地管理計算負擔，被認為是專用的硬體設備是必須的。

AI技術迅速的進步:

隨著AI演算法，尤其是機器學習和Deep學習的改良前進，與AI活動的計算要求複雜增加著。這個緣故，要滿足現代的AI應用的處理必要條件，變成更強有力有效的硬體設備·解決方案是必須的。

邊緣AI的需求高漲:

物聯網(IoT)設備劇增，隨著在網路·邊緣的實時處理和決策變得更重要，適合邊緣·運算被調整了的AI硬體設備的需求高漲著。使由於設備地方的能實行AI處理事，邊緣AI技術使之提高隱私，縮短等候時間，節約頻寬。

雲端基礎的AI服務的擴張:

為了支持AI工作負載的處理必要條件和儲存必要條件，對於大高科技企業的雲端基礎的AI服務需要強有力的硬體設備·基礎設施。被資料中心和雲端運算設施的AI最佳化了的硬體設備的需求，雲端基礎的AI服務的成長傳動高漲著。

AI硬體設備開發的投資:

政府，創業投資，多虧對科技企業的AI硬體設備的研究開發的大規模的投資，在這個領域革新劇增著。根據這樣的投資，被AI工作負載專用所製作的專用CPU，加速器，其他的硬體設備·零組件開發。

AI專用處理器的登場:

AI硬體設備，適合AI工作負載被設計了的FPGA(Field-Programmable Gate Arrays)，GPU(Graphics Processing Unit)，TPU(Tensor Processing Unit)，ASIC(Application-Specific Integrated 根據Circuit)等的專用處理器和加速器的開發，性能和能源效率提高著。

能源效率高(貴)的解決方案的需求:

能源效率與永續性，隨著含AI的工作負載的計算負擔提高，成為在AI硬體設備設計中越發重要的考慮事項。對最小限度一邊控制能源效率高(貴)的AI硬體設備·解決方案，對環境的負面影響，一邊削減運用成本和消耗功率。

全球人工智能AI硬體設備市場阻礙因素

人工智慧硬體設備市場，有幾個要素作為阻礙因素和課題起作用的可能性。

高(貴)的開發費:

AI硬體設備的製造，研究開發花費的費用有變得高額的可能性。為了要開發AI工作負載專門的處理器，加速器，其他的硬體設備·零組件大金額的研究開發費花費，中小企業有市場的進入躊躇的可能性。

整合:複雜

有在目前工作流程和系統合併AI硬體設備有，尤其是傳統的基礎設施的領域困難的情況。引進的門檻中，包含兼容性的問題，複雜的軟體整合，特定的商務上專門知識的必要性等。

熟練勞動力的點閱存取限制:

對現在，AI硬體設備的設計，開發，有最佳化的經驗的知識豐富的人力資源的需求提高著到供給以上。AI演算法，晶片設計，硬體設備工程等的領域的技術純熟勞工的不足，有妨礙AI硬體設備產業的新技術的開發與招聘的可能性。

與法規倫理的疑慮:

象AI硬體設備一樣的AI技術的使用，帶來偏見，隱私，保全，責任相關多的倫理的·法規的問題。AI硬體設備領域的企業，根據倫理標準的變化和預測不可能的法規，法律上的糾紛和傳聞傷害的風險高漲。

對資料隱私和保全的風險:

AI硬體設備為了頻繁處理機密資料，資料隱私和保全相關疑慮產生。AI硬體設備·系統的漏洞，有導致資料洩漏，不正當訪問，個人資料的濫用的可能性，損壞對這個技術的產業的信賴，阻礙那個普及。

互通性的課題:

根據各種各樣的AI硬體設備·平台和軟體·組成架構間的互通性標準和兼容性的缺乏，有在多種多樣的環境間的圓滑的整合和合作被阻礙的可能性。可擴展性，彈性，互通性，有根據互通性的問題被限制的可能性，要(會)妨礙AI硬體設備解決方案的招聘。

對環境的影響:

資料中心和雲端處理·基礎設施為首的AI齒輪的必要性高漲了的結果，更多的能源被使用，根據大氣中多的碳被放出。要減輕由於AI硬體設備的招聘的對環境的影響，需要致力於資源消費，能源效率，電子廢棄物管理的問題。

目錄

第1章 簡介

• 市場定義
• 市場區隔
• 調查手法

第2章 摘要整理

• 主要調查結果
• 市場概要
• 市場亮點

第3章 市場概要

• 市場規模與成長的可能性
• 市場趨勢
• 推動市場要素
• 阻礙市場要素
• 市場機會
• 波特的五力分析

第4章 人工智慧硬體設備市場:各元件類型

• 處理器
• 記憶體
• 儲存

第5章 人工智慧硬體設備市場:各用途

• 機器學習
• 深度學習
• 自然語言處理 (NLP)
• 電腦視覺
• 語音識別

第6章 人工智慧硬體設備市場:各終端用戶產業

• 醫療保健
• 汽車
• 零售
• 金融
• 製造業

第7章 地區分析

• 北美
• 美國
• 加拿大
• 墨西哥
• 歐洲
• 英國
• 德國
• 法國
• 義大利
• 亞太地區
• 中國
• 日本
• 印度
• 澳洲
• 南美
• 巴西
• 阿根廷
• 智利
• 中東·非洲
• 南非
• 沙烏地阿拉伯
• 阿拉伯聯合大公國

第8章 市場動態

• 推動市場要素
• 阻礙市場要素
• 市場機會
• COVID-19對市場的影響

第9章 競爭情形

• 主要企業
• 市場佔有率分析

第10章 企業簡介

• NVIDIA Corporation
• Intel Corporation
• IBM Corporation
• Qualcomm Technologies, Inc.
• Google LLC(Alphabet Inc.)
• Advanced Micro Devices, Inc.(AMD)
• Xilinx, Inc.
• Samsung Electronics Co., Ltd.
• Micron Technology, Inc.
• Amazon Web Services, Inc.(AWS)

第11章 市場展望和機會

• 新興技術
• 今後的市場趨勢
• 投資機會

第12章 附錄

• 簡稱清單
• 出處和參考文獻

Artificial Intelligence Ai Hardware Market Size And Forecast

Artificial Intelligence Ai Hardware Market size was valued at USD 54.10 Billion in 2023 and is projected to reach USD 474.10 Billion by 2030, growing at a CAGR of 38.73 % during the forecast period 2024-2030.

Global Artificial Intelligence Ai Hardware Market Drivers

The market drivers for the Artificial Intelligence Ai Hardware Market can be influenced by various factors. These may include: Growing AI Adoption in All Industries:

The demand for AI hardware is being driven by the broad use of AI in a number of industries, including healthcare, automotive, finance, retail, and manufacturing. AI is being used by industries for automation, data analytics, pattern recognition, and other purposes; to manage the computational load effectively, specialized hardware is required.

Fast Progress in AI Technology:

As AI algorithms continue to improve, especially in machine and deep learning, the computational demands and complexity of AI activities are rising. This makes more potent and effective hardware solutions necessary to meet the processing requirements of contemporary AI applications.

Growing Need for Edge AI:

As Internet of Things (IoT) devices proliferate and real-time processing and decision-making at network edges become more critical, there is an increasing need for AI hardware that is tailored for edge computing. By enabling devices to carry out AI operations locally, edge AI technology improves privacy, lowers latency, and conserves bandwidth.

Extension of Cloud-based AI Services:

To support the processing and storage requirements of AI workloads, large tech companies' cloud-based AI services require a strong hardware infrastructure. The need for AI-optimized hardware in data centers and cloud computing facilities is rising in tandem with the growth of cloud-based AI services.

Investments in AI Hardware Development:

The field is experiencing a surge in innovation thanks to large investments made in AI hardware research and development by governments, venture capitalists, and technology corporations. With the help of these investments, dedicated CPUs, accelerators, and other hardware components made especially for AI workloads are being developed.

Emergence of AI-specific Processors:

AI hardware is seeing performance and energy efficiency improvements as a result of the development of specialized processors and accelerators, such as Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Application-Specific Integrated Circuits (ASICs), designed for AI workloads.

Demand for Energy-efficient Solutions:

Energy efficiency and sustainability are becoming more and more important considerations in AI hardware design as workloads involving AI become more computationally demanding. Energy-efficient AI hardware solutions minimize their negative effects on the environment while lowering operational expenses and power consumption.

Global Artificial Intelligence Ai Hardware Market Restraints

Several factors can act as restraints or challenges for the Artificial Intelligence Ai Hardware Market. These may include:

High Development expenses:

The expenses of manufacturing, research, and development for AI hardware can be high. Smaller businesses may be discouraged from entering the market by the substantial R&D costs involved in creating specialized processors, accelerators, and other hardware components for AI workloads.

Complexity of Integration:

It can be difficult to integrate AI hardware into current workflows and systems, particularly in sectors with legacy infrastructure. Adoption hurdles may include compatibility problems, complicated software integration, and the requirement for specialist knowledge in particular businesses.

Restricted Access to Skilled Workforce:

There is now a greater need than supply for knowledgeable individuals with experience in AI hardware design, development, and optimization. The lack of skilled workers in fields like AI algorithms, chip design, and hardware engineering may impede the development and adoption of new technologies in the AI hardware industry.

Regulatory and Ethical Concerns:

The use of AI technology, such as AI hardware, brings up a number of ethical and regulatory issues pertaining to bias, privacy, security, and responsibility. Companies in the AI hardware sector run a greater risk of legal trouble as well as reputational damage due to changing ethical standards and unpredictable regulations.

Risks to Data Privacy and Security:

AI hardware handles sensitive data frequently, which gives rise to worries about data privacy and security. AI hardware system vulnerabilities could result in data breaches, unauthorized access, and misuse of personal data, eroding industry confidence in the technology and impeding its widespread implementation.

Interoperability Challenges:

Smooth integration and cooperation across diverse environments can be impeded by a lack of interoperability standards and compatibility across various AI hardware platforms and software frameworks. Scalability, flexibility, and interoperability may be restricted by interoperability issues, which would impede the adoption of AI hardware solutions.

Environmental Impact:

More energy is used and more carbon is released into the atmosphere as a result of the growing need for AI gear, notably data centers and cloud computing infrastructure. Mitigating the environmental impact of AI hardware adoption requires addressing issues with resource consumption, energy efficiency, and electronic waste management.

Global Artificial Intelligence Ai Hardware Market Segmentation Analysis

The Global Artificial Intelligence Ai Hardware Market is Segmented on the basis of Component Type, Application, End-user Industry, and Geography.

Artificial Intelligence Ai Hardware Market, By Component Type

• Processors:
• Central Processing Units (CPUs), Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs) are some of the processor types included in this section that are optimized for AI workloads.)
• Memory:
• High Bandwidth Memory (HBM), Graphics Double Data Rate (GDDR) memory, and other high-performance memory solutions are included in this segment. These components are specifically made for AI applications.
• Storage:
• Solid-State Drives (SSDs), Non-Volatile Memory Express (NVMe) storage, and other storage technologies targeted for quick access and processing of big datasets are included in this sector of storage solutions designed for AI workloads.

Artificial Intelligence Ai Hardware Market, By Application

• Machine Learning:
• AI hardware for machine learning applications, such as pattern recognition, classification, regression, clustering, and anomaly detection, is included in this subsegment.
• Deep Learning:
• This subsegment includes hardware accelerators and processors specifically intended for deep learning activities, such as neural network training and inference.
• Natural Language Processing (NLP):
• This subsegment includes AI hardware that has been tuned to process and comprehend natural language speech and text data.
• Computer Vision:
• This subsegment includes hardware components designed for computer vision applications, such as object identification, scene understanding, and image recognition.
• Speech Recognition:
• AI hardware for voice synthesis, speech recognition, and other audio processing applications falls under this subsegment.

Artificial Intelligence Ai Hardware Market, By End-user Industry

• Healthcare:
• AI hardware solutions are used in drug discovery, clinical decision support systems, medical imaging analysis, and customized medicine, among other healthcare applications.
• Automotive:
• This subsegment includes hardware components used in advanced driver-assistance systems (ADAS), driverless vehicles, vehicle perception, and control systems.
• Retail:
• Applications including demand forecasting, inventory management, consumer analytics, and tailored marketing are implemented using AI hardware in the retail sector.
• Finance:
• Hardware solutions for algorithmic trading, fraud detection, risk assessment, and customer care automation are utilized in financial services.
• Manufacturing:
• Artificial intelligence hardware is used in applications including process automation, supply chain optimization, quality assurance, and predictive maintenance in the manufacturing sector.

Artificial Intelligence Ai Hardware Market, By Geography

• North America:
• This subsegment covers the market for AI hardware in nations like the US and Canada, where the use of AI technologies is widely used in a variety of industries.
• Europe:
• This subsegment covers the AI hardware market in nations including the United Kingdom, Germany, France, and others in Europe.
• Asia Pacific:
• This subsegment focuses on the AI hardware market in nations with fast expanding economies, including South Korea, China, Japan, India, and Southeast Asia.
• Latin America:
• This subsegment includes the AI hardware markets in nations such as Brazil, Mexico, Argentina, and others.
• Middle East and Africa:
• This subsegment covers the AI hardware market in these nations, where the use of AI technology is growing in industries including banking, healthcare, and oil & gas.

Key Players

• The major players in the Artificial Intelligence Ai Hardware Market are:
• NVIDIA Corporation
• Intel Corporation
• IBM Corporation
• Qualcomm Technologies, Inc.
• Google LLC (Alphabet Inc.)
• Advanced Micro Devices, Inc. (AMD)
• Xilinx, Inc.
• Samsung Electronics Co., Ltd.
• Micron Technology, Inc.
• Amazon Web Services, Inc. (AWS)

TABLE OF CONTENTS

1. Introduction

• Market Definition
• Market Segmentation
• Research Methodology

2. Executive Summary

• Key Findings
• Market Overview
• Market Highlights

3. Market Overview

• Market Size and Growth Potential
• Market Trends
• Market Drivers
• Market Restraints
• Market Opportunities
• Porter's Five Forces Analysis

4. Artificial Intelligence Ai Hardware Market, By Component Type

• Processors
• Memory
• Storage

5. Artificial Intelligence Ai Hardware Market, By Application

• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Speech Recognition

6. Artificial Intelligence Ai Hardware Market, By End-user Industry

• Healthcare
• Automotive
• Retail
• Finance
• Manufacturing

7. Regional Analysis

• North America
• United States
• Canada
• Mexico
• Europe
• United Kingdom
• Germany
• France
• Italy
• Asia-Pacific
• China
• Japan
• India
• Australia
• Latin America
• Brazil
• Argentina
• Chile
• Middle East and Africa
• South Africa
• Saudi Arabia
• UAE

8. Market Dynamics

• Market Drivers
• Market Restraints
• Market Opportunities
• Impact of COVID-19 on the Market

9. Competitive Landscape

• Key Players
• Market Share Analysis

10. Company Profiles

• NVIDIA Corporation
• Intel Corporation
• IBM Corporation
• Qualcomm Technologies, Inc.
• Google LLC (Alphabet Inc.)
• Advanced Micro Devices, Inc. (AMD)
• Xilinx, Inc.
• Samsung Electronics Co., Ltd.
• Micron Technology, Inc.
• Amazon Web Services, Inc. (AWS)

11. Market Outlook and Opportunities

• Emerging Technologies
• Future Market Trends
• Investment Opportunities

12. Appendix

• List of Abbreviations
• Sources and References