AI 클라우드 워크로드

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.

TABLE OF CONTENTS

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

Charts

• Chart 1 AI External Workload Capacity Consumption by Type
• Chart 2 AI Training Workload Capacity Consumption by Region
• Chart 3 AI Inference Workload Capacity Consumption by Cloud Service Provider
• Chart 4 AI Inference Workload Capacity Consumption by Region
• Chart 5 AI Inference Workload Capacity Consumption by Type
• Chart 6 AI Inference Workload Capacity Consumption by Type