Introduction
Kubernetes enables scalability — but AI workloads introduce new challenges.
GPU scheduling and orchestration require careful configuration.
Key Requirements
- GPU-aware node labeling
- Resource allocation policies
- Network alignment
- Persistent storage integration
Performance Considerations
- Avoid noisy neighbor issues
- Monitor GPU utilization
- Optimize pod-to-pod latency
Conclusion
Kubernetes must be engineered to support AI — not treated as a generic container platform.