Scaling Challenges for AI
AI systems present unique scaling challenges due to compute intensity, model size, and state management. Learn strategies for building scalable AI infrastructure.
Horizontal Scaling Patterns
Scale out AI services across multiple instances:
Load Balancing Strategies
Session Affinity
Maintain context across requests:
- Sticky sessions for stateful models
- Distributed cache for context
- Session migration strategies
- Graceful session draining
Model Serving Optimization
Optimize model serving for scale:
Auto-scaling Configuration
Dynamically adjust resources based on demand:
Data Pipeline Scaling
Scale data processing for AI workloads:
Distributed Processing
- Partition data by key
- Use message queues for decoupling
- Implement idempotent processing
- Handle partial failures gracefully
Performance Optimization
- Use GPU scheduling efficiently
- Implement request coalescing
- Optimize model loading time
- Use edge caching where possible
- Profile and optimize hot paths