人工智慧與機器學習：市場概覽：2024年第3 季

本報告考察了人工智慧和機器學習市場，包括對配備邊緣人工智慧晶片組的設備出貨量的預測、依行業和地區進行的詳細分析以及雲端和邊緣AI晶片架構演進等總結。

實際好處：

• 確定人工智慧解決方案在特定產業的成長機會，並制定適當的資訊和策略
• 讓內部和外部利害關係人了解人工智慧對邊緣和雲端晶片組出貨量以及收入的影響
• 可根據晶片組架構的相對成長來評估策略

重要問題的答案：

• 到2030年，人工智慧硬體將如何應用於邊緣設備、網路閘道和伺服器？
• 隨著時間的推移，全球和區域範圍內的雲端和邊緣人工智慧市場將如何發展？邊緣AI將如何在各行業實施？
• 到2030年，哪種晶片組架構將主導人工智慧？

研究亮點：

• 按區域、架構、產業、工作負載和用例對設備、網路閘道和本機伺服器進行詳細分析
• 搭載邊緣 AI 晶片組的設備數量：17個產業和 4個地區
• 雲端和邊緣的AI 晶片組架構的演變

目錄

主要發現

主要建議

新資訊

調查方法

重要預測

相關研究

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