人工智慧硬体设备的全球市场规模:各元件类型，各用途，各终端用户产业，各地区，范围及预测

人工智慧硬体设备的市场规模与预测

人工智慧硬体设备市场规模，被预测2023年541亿美元被估价，从2024年开始2030年的在预测期间内38.73 %的年复合成长率增长，2030年达到4,741亿美元。

人工智慧硬体设备的全球市场促进因素

人工智慧硬体设备市场推动市场要素，有受到各式各样的因素的影响的可能性。

AI硬体设备的需求，由于医疗保健，汽车，金融，零售，製造等，多的产业AI广泛地所使用的事变成要素。AI，以自动化，资料分析，模式认识等的目的各产业被利用，为了效率性地管理计算负担，被认为是专用的硬体设备是必须的。

AI技术迅速的进步:

随着AI演算法，尤其是机器学习和Deep学习的改良前进，与AI活动的计算要求复杂增加着。这个缘故，要满足现代的AI应用的处理必要条件，变成更强有力有效的硬体设备·解决方案是必须的。

边缘AI的需求高涨:

物联网(IoT)设备剧增，随着在网路·边缘的实时处理和决策变得更重要，适合边缘·运算被调整了的AI硬体设备的需求高涨着。使由于设备地方的能实行AI处理事，边缘AI技术使之提高隐私，缩短等候时间，节约频宽。

云端基础的AI服务的扩张:

为了支持AI工作负载的处理必要条件和储存必要条件，对于大高科技企业的云端基础的AI服务需要强有力的硬体设备·基础设施。被资料中心和云端运算设施的AI最佳化了的硬体设备的需求，云端基础的AI服务的成长传动高涨着。

AI硬体设备开发的投资:

政府，创业投资，多亏对科技企业的AI硬体设备的研究开发的大规模的投资，在这个领域革新剧增着。根据这样的投资，被AI工作负载专用所製作的专用CPU，加速器，其他的硬体设备·零组件开发。

AI专用处理器的登场:

AI硬体设备，适合AI工作负载被设计了的FPGA(Field-Programmable Gate Arrays)，GPU(Graphics Processing Unit)，TPU(Tensor Processing Unit)，ASIC(Application-Specific Integrated 根据Circuit)等的专用处理器和加速器的开发，性能和能源效率提高着。

能源效率高(贵)的解决方案的需求:

能源效率与永续性，随着含AI的工作负载的计算负担提高，成为在AI硬体设备设计中越发重要的考虑事项。对最小限度一边控制能源效率高(贵)的AI硬体设备·解决方案，对环境的负面影响，一边削减运用成本和消耗功率。

全球人工智能AI硬体设备市场阻碍因素

人工智慧硬体设备市场，有几个要素作为阻碍因素和课题起作用的可能性。

高(贵)的开发费:

AI硬体设备的製造，研究开发花费的费用有变得高额的可能性。为了要开发AI工作负载专门的处理器，加速器，其他的硬体设备·零组件大金额的研究开发费花费，中小企业有市场的进入踌躇的可能性。

整合:复杂

有在目前工作流程和系统合併AI硬体设备有，尤其是传统的基础设施的领域困难的情况。引进的门槛中，包含兼容性的问题，复杂的软体整合，特定的商务上专门知识的必要性等。

熟练劳动力的点阅存取限制:

对现在，AI硬体设备的设计，开发，有最佳化的经验的知识丰富的人力资源的需求提高着到供给以上。AI演算法，晶片设计，硬体设备工程等的领域的技术纯熟劳工的不足，有妨碍AI硬体设备产业的新技术的开发与招聘的可能性。

与法规伦理的疑虑:

象AI硬体设备一样的AI技术的使用，带来偏见，隐私，保全，责任相关多的伦理的·法规的问题。AI硬体设备领域的企业，根据伦理标准的变化和预测不可能的法规，法律上的纠纷和传闻伤害的风险高涨。

对资料隐私和保全的风险:

AI硬体设备为了频繁处理机密资料，资料隐私和保全相关疑虑产生。AI硬体设备·系统的漏洞，有导致资料洩漏，不正当访问，个人资料的滥用的可能性，损坏对这个技术的产业的信赖，阻碍那个普及。

互通性的课题:

根据各种各样的AI硬体设备·平台和软体·组成架构间的互通性标准和兼容性的缺乏，有在多种多样的环境间的圆滑的整合和合作被阻碍的可能性。可扩展性，弹性，互通性，有根据互通性的问题被限制的可能性，要(会)妨碍AI硬体设备解决方案的招聘。

对环境的影响:

资料中心和云端处理·基础设施为首的AI齿轮的必要性高涨了的结果，更多的能源被使用，根据大气中多的碳被放出。要减轻由于AI硬体设备的招聘的对环境的影响，需要致力于资源消费，能源效率，电子废弃物管理的问题。

目录

第1章 简介

• 市场定义
• 市场区隔
• 调查手法

第2章 摘要整理

• 主要调查结果
• 市场概要
• 市场亮点

第3章 市场概要

• 市场规模与成长的可能性
• 市场趋势
• 推动市场要素
• 阻碍市场要素
• 市场机会
• 波特的五力分析

第4章 人工智慧硬体设备市场:各元件类型

• 处理器
• 记忆体
• 储存

第5章 人工智慧硬体设备市场:各用途

• 机器学习
• 深度学习
• 自然语言处理 (NLP)
• 电脑视觉
• 语音识别

第6章 人工智慧硬体设备市场:各终端用户产业

• 医疗保健
• 汽车
• 零售
• 金融
• 製造业

第7章 地区分析

• 北美
• 美国
• 加拿大
• 墨西哥
• 欧洲
• 英国
• 德国
• 法国
• 义大利
• 亚太地区
• 中国
• 日本
• 印度
• 澳洲
• 南美
• 巴西
• 阿根廷
• 智利
• 中东·非洲
• 南非
• 沙乌地阿拉伯
• 阿拉伯联合大公国

第8章 市场动态

• 推动市场要素
• 阻碍市场要素
• 市场机会
• COVID-19对市场的影响

第9章 竞争情形

• 主要企业
• 市场占有率分析

第10章 企业简介

• 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 市场展望和机会

• 新兴技术
• 今后的市场趋势
• 投资机会

第12章 附录

• 简称清单
• 出处和参考文献

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