This image visualizes the three major AI workload types and their characteristics in a comprehensive graph.
Graph Structure Analysis
Visualization Framework:
- Y-axis: AI workload intensity (requests per hour, FLOPS, CPU/GPU utilization, etc.)
- X-axis: Time progression
- Stacked Area Chart: Shows the proportion and changes of three workload types within the total AI system load
Three AI Workload Characteristics
1. Learning – Blue Area
Properties: Steady, Controllable, Planning
- Located at the bottom with a stable, wide area
- Represents model training processes with predictable and plannable resource usage
- Maintains consistent load over extended periods
2. Reasoning – Yellow Area
Properties: Fluctuating, Unpredictable, Optimizing!!!
- Middle layer showing dramatic fluctuations
- Involves complex decision-making and logical reasoning processes
- Most unpredictable workload requiring critical optimization
- Load varies significantly based on external environmental changes
3. Inference – Green Area
Properties: On-device Side, Low Latency
- Top layer with irregular patterns
- Executes on edge devices or user terminals
- Service workload requiring real-time responses
- Low latency is the core requirement
Key Implications
Differentiated Resource Management Strategies Required:
- Learning: Stable long-term planning and infrastructure investment
- Reasoning: Dynamic scaling and optimization technology focus
- Inference: Edge optimization and response time improvement
This graph provides crucial insights demonstrating that customized resource allocation strategies considering the unique characteristics of each workload type are essential for effective AI system operations.
This visualization emphasizes that AI workloads are not monolithic but consist of distinct components with varying demands, requiring sophisticated resource management approaches to handle their collective and individual requirements effectively.
With Claude
Lechuck Park 