人工智慧與機器學習：雲端人工智慧市場數據概覽（2025 年第三季）

本報告分析了雲端人工智慧晶片組市場，並提供了人工智慧晶片組出貨量和收入的預測，以及按地區、架構、工作負載和人工智慧框架劃分的區隔數據。

實際優勢：

• 預測到 2031 年雲端人工智慧晶片組的市場規模（出貨量和收入）。
• 了解主要架構（例如 GPU）和不同地區雲端人工智慧晶片組出貨量和收入的成長。
• 根據主要人工智慧框架的成長率以及雲端人工智慧中推理和學習之間的平衡，評估相關策略。
• 比較雲端和邊緣環境中人工智慧晶片組的市場規模。

回答關鍵問題：

• 到 2031 年，AI 晶片組在伺服器的應用程度如何？
• AI 晶片組市場將如何依地區發展？
• 這些晶片組主要面向哪些 AI 框架？
• 到 2031 年，哪種晶片組架構將主導雲端 AI？

研究亮點：

• 2024年至2031年雲端市場AI晶片組出貨量與營收預測
• 按地區、架構、工作負載和AI框架劃分的詳細分析

目錄

本產品應與 "人工智慧機器學習雲AI" （MD-AIMLCL-103）搭配使用。

主要發現

最新發布

研究方法

主要預測

Actionable Benefits:

• Identify the market size (shipments and revenue) of Artificial Intelligence (AI) chipsets in cloud environments through 2031.
• Understand how cloud AI chipset shipments and revenue will grow by key regions and architectures, e.g., Graphics Processing Unit (GPU).
• Evaluate strategy based on relative growth of key AI frameworks and inference versus training in cloud AI.
• Compare the market size of AI chipsets in cloud to edge environments.

Critical Questions Answered:

• How significant will AI chipsets adoption be in servers until 2031?
• How will the AI chipset market for each region develop going forward?
• Which AI frameworks will these chipsets target?
• Which chipset architecture will dominate cloud AI by 2031?

Research Highlights:

• Forecasts for AI chipset shipments and revenue in the cloud market from 2024 to 2031.
• Detailed breakdowns by region, architecture, workload, and AI framework.

Who Should Read This?

• Chipset vendors seeking greater insight into their market sizes and new growth areas.
• Sales and marketing leaders in diverse markets impacted by AI.
• Investors gauging the potential for growth in cloud AI.
• Strategists across the entire AI stack developing cloud AI products and marketing.

TABLE OF CONTENTS

This product is meant to be read in conjunction with Artificial Intelligence Machine Learning Cloud AI MD-AIMLCL-103

Key Findings

Whats New

Methodology

Significant Forecasts