AI Cost Optimization Advanced

This lesson covers practical techniques to reduce AI infrastructure costs by 30-60% without sacrificing performance. From right-sizing GPU instances to leveraging spot pricing and intelligent scheduling, these optimization strategies apply to both cloud and on-premise GPU environments.

Right-Sizing GPU Instances

Spot and Preemptible Instances

Spot instances offer 60-90% savings but can be interrupted. Use them for:

• Training with checkpointing - Save checkpoints every N steps; restart from latest checkpoint on interruption
• Hyperparameter search - Run many short experiments; losing one is acceptable
• Batch inference - Process non-time-sensitive inference requests in large batches
• Data preprocessing - Feature engineering and data transformation pipelines

Scheduling Optimization

• Off-peak scheduling - Run training jobs during nights and weekends when GPU demand (and spot prices) are lower
• Job packing - Use MIG or time-slicing to pack multiple small workloads onto a single GPU
• Preemption policies - Allow high-priority inference to preempt low-priority training on shared clusters
• Auto-scaling - Scale inference endpoints to zero during no-traffic periods

Architecture Optimization

• Model distillation - Train a smaller model that mimics a larger one; cheaper to serve
• Quantization - Run INT8 inference instead of FP32; same GPU handles 2-4x more requests
• Caching - Cache frequent inference results to reduce GPU compute needs
• Batching - Batch inference requests to maximize GPU throughput per dollar