Compute Tiers for AI Architecture Intermediate

A well-designed compute tier strategy ensures that every AI workload runs on the most cost-effective infrastructure that meets its performance requirements. This lesson covers how to architect multi-tier compute hierarchies that balance GPU availability, cost efficiency, and operational simplicity.

The Four Compute Tiers

GPU Pool Architecture

Centralized GPU pools shared across teams provide better utilization and cost efficiency than dedicated instances:

• Kubernetes GPU scheduling - Use node selectors and tolerations to route workloads to the right GPU tier
• GPU time-slicing - Share a single GPU across multiple inference workloads using MPS or MIG
• Priority queuing - Implement priority-based scheduling so production inference takes precedence over training experiments
• Quota management - Set per-team GPU quotas to prevent resource monopolization

Scaling Strategies by Tier

• Tier 1 - Pre-provisioned reserved capacity. Scale by adding nodes to the training cluster. Use checkpointing for fault tolerance.
• Tier 2 - Cluster autoscaler with mixed on-demand and spot instances. Scale based on pending pod count.
• Tier 3 - Aggressive spot usage (90%+). Scale based on queue depth or CPU utilization.
• Tier 4 - Automatic scaling to zero. No capacity planning required.