How We Cut ML Inference Costs by 68% Without Losing Accuracy

We helped a client reduce ML inference costs from $47,000 to $15,000 per month — a 68% reduction — without measurable accuracy loss. Here is the exact engineering playbook.

The Cost Problem

A transformer NLP model: 96.8% accuracy, 45ms latency, but four A100 GPUs at $47,000/month. As volume grew, costs scaled linearly and the business case eroded.

Model Distillation

Trained an 85M-parameter student to mimic the 350M-parameter teacher, retaining 99.2% accuracy using soft label transfer.

INT8 Quantization

Post-training quantization from FP32 to INT8 reduced memory 4x with less than 0.1% accuracy drop.

Dynamic Batching + Spot Instances

Grouped requests into GPU passes for 3x throughput. Spot instances at 60-70% discount. Final: $15,000/month, 96.1% accuracy, 32ms latency. QuantPi.ai applies this playbook to every production deployment.