人工智慧与机器学习：市场概览：2024年第3 季

本报告考察了人工智慧和机器学习市场，包括对配备边缘人工智慧晶片组的设备出货量的预测、依行业和地区进行的详细分析以及云端和边缘AI晶片架构演进等总结。

实际好处：

• 确定人工智慧解决方案在特定产业的成长机会，并制定适当的资讯和策略
• 让内部和外部利害关係人了解人工智慧对边缘和云端晶片组出货量以及收入的影响
• 可根据晶片组架构的相对成长来评估策略

重要问题的答案：

• 到2030年，人工智慧硬体将如何应用于边缘设备、网路闸道和伺服器？
• 随着时间的推移，全球和区域范围内的云端和边缘人工智慧市场将如何发展？边缘AI将如何在各行业实施？
• 到2030年，哪种晶片组架构将主导人工智慧？

研究亮点：

• 按区域、架构、产业、工作负载和用例对设备、网路闸道和本机伺服器进行详细分析
• 搭载边缘 AI 晶片组的设备数量：17个产业和 4个地区
• 云端和边缘的AI 晶片组架构的演变

目录

主要发现

主要建议

新资讯

调查方法

重要预测

相关研究

Actionable Benefits:

• Identify growth opportunities and formulate the right messaging and strategies for Artificial Intelligence (AI) solutions in specific verticals.
• Educate internal and external stakeholders about the impact of AI on chipset shipments and revenue both at the edge and in the cloud.
• Evaluate strategy based on the relative growth of chipset architectures.

Critical Questions Answered:

• How will AI hardware be adopted in edge devices, gateways, and servers until 2030?
• How will the cloud and edge AI market evolve over time, both globally and regionally?
• How will edge AI be implemented across different verticals?
• Which chipset architecture will dominate AI by 2030?

Research Highlights:

• Detailed coverage of devices, gateways, and on-premises servers by region, architecture, vertical, workload, and use case.
• Shipments of devices with edge AI chipsets by 17 verticals and 4 regions.
• Evolution of AI chipset architecture in the cloud and at the edge.

Who Should Read This?

• Product strategists at AI chipset vendors.
• AI solution providers looking to understand short-term and long-term vertical-specific opportunities.
• Investors gauging the potential for growth in the markets covered.
• Sales and marketing leaders in diverse markets effected by AI.

TABLE OF CONTENTS

Key Findings

Key Recommendations

Whats New

Methodology

Significant Forecasts

Related Research