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

The rightsizing audit collects GPU utilisation, latency, and QPS distribution per service. Most production inference workloads sit at 30-50% utilisation, which is 2x overprovisioning; latency well within SLO indicates rightsizing room; bursty workloads need headroom while steady workloads can run hot.

• GPU utilisation per service. p50, p95, p99; 30-50% utilisation is 2x overprovisioning, which is the typical state.
• Latency p99 vs SLO. Well within SLO means rightsizing room; tight latency means rightsizing risks SLO breach.
• QPS distribution. Bursty workloads need headroom; steady workloads can run hot.
• Per-service audit deliverable. Documented utilisation, latency, QPS shape; supports rightsizing decisions.

Concrete rightsizing steps

Rightsizing follows three concrete steps. Step 1: shrink replica count by 25%, measure latency and utilisation, repeat if both hold. Step 2: switch to a smaller GPU instance type (A100 to A10 often works for inference). Step 3: enable auto-scaling so steady-state pays the baseline and bursts pay only when they happen.

• Step 1: shrink replica count. Start with -25%; measure latency and utilisation; if both hold, keep going.
• Step 2: switch instance type. A100 to A10 often works for inference workloads that don’t need flagship throughput.
• Step 3: enable auto-scaling. Steady-state baseline plus scale-up on demand; bursts paid only when they happen.
• Per-step measurement gate. Each step gated on metrics holding; rollback is the default if they don’t.

What teams have achieved

The savings are real and predictable. Median 60% reduction in GPU spend across 8 production services with a range of 30% to 80%; latency typically improved 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.

• Median 60% spend reduction. Across 8 production services; range 30% to 80%.
• Latency improvement. 5-10% better post-rightsizing because hot GPUs are more efficient than cold ones.
• No quality regression. The model didn’t change; only the placement.
• Per-service before/after. Documented baseline and post-rightsizing numbers; supports continued investment.

What to watch

Rightsizing has predictable risks. Burst capacity is the largest: auto-scaling has 30-90 second startup lag for GPU instances and bursty workloads may breach SLO during scale-up. Tail latency degrades more than median on smaller instances; vendor pricing changes can displace today’s optimal instance type by next quarter, so re-audit quarterly.

• Burst capacity. Auto-scaling has 30-90 second startup lag for GPU instances; bursty workloads may breach SLO.
• 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.
• Quarterly re-audit. Pricing and workload shape both shift; the optimal placement is not static.