How We Reduced Model Inference Latency by 73% Without Sacrificing Accuracy
When our analytics platform crossed 50,000 concurrent users, our ML inference pipeline started showing cracks. P99 latency spiked from 180ms to over 2.4 seconds during peak hours. This is the story of how we diagnosed the problem and built a solution that brought us back under 200ms.
Diagnosing the Bottleneck
The first step was profiling. We instrumented our inference pipeline with OpenTelemetry and discovered that 68% of our latency budget was being spent on model loading — we were cold-loading models on every request because our feature store was not maintaining warm instances for low-traffic model variants.
The Solution: Tiered Model Warming
We implemented a three-tier warming strategy: hot (always-loaded for top 20 models), warm (loaded within 5s for the next 200 models), and cold (on-demand for the long tail). Combined with ONNX quantization for the hot tier, we achieved the 73% latency reduction while keeping accuracy degradation below 0.3%.
Key metric: P99 latency dropped from 2,412ms → 647ms with tiered warming alone. Quantization brought it further to 183ms.
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