GPU Cost Optimization for AI Workloads
Overview
GPUs are essential for AI workloads but can be expensive. This guide covers strategies to optimize GPU usage and reduce costs.
Cost Challenges
- Idle GPU time
- Over-provisioning
- Expensive on-demand pricing
Architecture Approach
- Use spot/preemptible instances
- Autoscaling GPU clusters
- Multi-tenant GPU scheduling
Optimization Techniques
- Job scheduling
- Model quantization
- Mixed precision training
Tools Used
- Kubernetes
- Ray
- NVIDIA Triton
Best Practices
- Monitor GPU utilization
- Use right-sized instances
- Schedule jobs during off-peak hours