AI & ML Practical By Samson Tanimawo, PhD Published Jul 27, 2026 4 min read

Inference Rightsizing: How to Cut GPU Wastage by 60%

Most inference workloads are over-provisioned by 2-3x. The rightsizing audit, with concrete steps and the savings teams have actually achieved.

The audit

Pull GPU utilisation per service, p50, p95, p99. Most production inference workloads sit at 30-50% utilisation. That is overprovisioning by 2x.

Pull latency p99. If latency is well within SLO, there is rightsizing room. Tight latency means rightsizing risks SLO breach.

Pull QPS distribution. Bursty workloads need headroom; steady workloads can run hot.

Step 1: shrink replica count. Start with -25%. Measure latency and utilisation; if both hold, keep going.

Step 2: switch instance type. Smaller GPUs (e.g., A100 → A10) often work for inference workloads that do not need flagship throughput.

Step 3: enable auto-scaling. Steady-state baseline + scale-up on demand. Catches bursts without paying for them at idle.

Median 60% reduction in GPU spend across 8 production services. Range: 30% to 80%.

Latency typically improved by 5-10% post-rightsizing because hot GPUs are more efficient than cold ones.

No quality regressions because the model itself did not change; only the placement.

Burst capacity. Auto-scaling has a startup lag (30-90 seconds for GPU instances). Bursty workloads may breach SLO during scale-up.

Tail latency. Smaller instances have less throughput; p99 may degrade more than p50.

Vendor pricing changes. Today's optimal instance type can be displaced by next quarter's pricing. Re-audit quarterly.