AI雲端工作負載

AI Cloud Workloads

出版日期: 2026年05月12日 | 出版商:

ABI Research | 英文 12 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

本報告調查了AI雲端工作負載的趨勢，總結了訓練和推理工作負載消耗的趨勢和預測，並依類型、雲端服務供應商和地區等各種類別進行了詳細分析。

實際益處：

了解訓練和推理處理能力如何隨時間擴展，可以幫助自信地制定能力策略。
透過清楚了解工作負載成長模式和轉折點，可以最佳化AI 投資。
預測長期影響基礎設施採購的區域趨勢。

主要問題解答：

在雲端運算中，推理何時會超越訓練，成為主要的資源來源？
在雲端，哪種類型的推理工作負載會消耗最多的容量？
不同地區的推理和訓練過渡點有何不同？

研究亮點：

對供應商細分市場進行全面分析：一級和二級超大規模資料中心業者、新型雲端服務供應商和主權雲端服務供應商。
對成長軌跡和轉折點進行詳細分析（包括推理工作負載優於訓練工作負載的時期）
到2035年雲端 AI 推理工作負載的詳細預測

圖 1：AI外部工作負載容量消耗：依類型分類
圖 2：AI訓練工作負載容量消耗：依地區分類
圖 3：AI推理工作負載容量消耗：依雲端服務供應商分類
圖 4：AI推理工作負載容量消耗：依地區分類
圖 5：AI推理工作負載的容量消耗：依類型分類
圖 6：AI推理工作負載的容量消耗：依類型分類

簡介目錄

Product Code: MD-AICW-101

Actionable Benefits:

Plan capacity strategies with confidence by understanding how training and inference capacity will grow over time.
Align Artificial Intelligence (AI) investments with clear visibility into workload growth patterns and inflection points.
Anticipate regional dynamics that will shape infrastructure procurement over time.

Critical Questions Answered:

When will inference surpass training as the dominant capacity consumer in the cloud?
Which types of inference workloads will be consuming the most capacity in the cloud?
How will the inference-training inflection point differ by region?

Research Highlights:

Comprehensive analysis of operator segments: Tier One and Tier Two hyperscalers, neocloud providers, and sovereign clouds.
Detailed research into growth trajectories and inflection points-including when inference workloads will overtake training workloads.
A detailed forecast of AI inference workloads in the cloud up to 2035.

Who Should Read This?

Cloud and data center strategy leaders looking to optimize their procurement and investment roadmaps.
Infrastructure developers and ecosystem partners aiming to anticipate regional trends.
AI compute leaders looking to refine their roadmaps and strategies.

Tables

Table 1 AI Cloud External Workload Capacity Consumption by Cloud Service Provider
Table 2 AI Cloud External Workload Capacity Consumption by Region
Table 3 AI Cloud Workload Capacity Consumption by Instance
Table 4 AI External Workload Capacity Consumption by Type
Table 5 AI Training Workload Capacity Consumption by Type
Table 6 AI Inference Workload Capacity Consumption by Type
Table 7 AI Training Workload Capacity Consumption by Cloud Service Provider
Table 8 AI Training Workload Capacity Consumption by Region
Table 9 Foundation Model Training Capacity Consumption by Cloud Service Provider
Table 10 Foundation Model Training Capacity Consumption by Region
Table 11 AI Fine-Tuning Capacity Consumption by Cloud Service Provider
Table 12 AI Fine-Tuning Capacity Consumption by Region
Table 13 Traditional ML Training Capacity Consumption by Cloud Service Provider
Table 14 Traditional ML Training Capacity Consumption by Region
Table 15 AI Inference Workload Capacity Consumption by Cloud Service Provider
Table 16 AI Inference Workload Capacity Consumption by Region
Table 17 Text Generation Capacity Consumption by Cloud Service Provider
Table 18 Text Generation Capacity Consumption by Region
Table 19 Code Generation Capacity Consumption by Cloud Service Provider
Table 20 Code Generation Capacity Consumption by Region
Table 21 Image Generation Capacity Consumption by Cloud Service Provider
Table 22 Image Generation Capacity Consumption by Region
Table 23 Video Generation Capacity Consumption by Cloud Service Provider
Table 24 Video Generation Capacity Consumption by Region
Table 25 Audio Generation Capacity Consumption by Cloud Service Provider
Table 26 Audio Generation Capacity Consumption by Region
Table 27 Other Inference Workload Capacity Consumption by Cloud Service Provider
Table 28 Other Inference Workload Capacity Consumption by Region