Quantization Failure Modes and Mitigation Strategies
Quantization can fail in subtle ways that only appear in production. Understanding these failure modes helps you build robust quantized systems.
Accuracy Collapse
Some layers are quantization-sensitive. Attention layers in transformers and the first/last layers in CNNs often suffer disproportionate accuracy loss. Fix with mixed precision - keep sensitive layers in FP16.
Outlier activations cause range issues. A few extreme values force wide quantization ranges, reducing precision for normal values. Use per-channel quantization or outlier clipping.
Calibration Failures
Calibration data mismatch is common. If calibration data differs from production, quantization ranges are wrong. Use at least 1000 representative samples across all categories.
Deployment Issues
Hardware mismatch breaks performance. Models optimized for one GPU may not run efficiently on another. Always benchmark on target hardware.
Operator coverage gaps occur when frameworks lack quantized versions of some operators. Ops fall back to FP32, creating memory overhead that negates speedups.
Warning: Numerical instability often manifests as NaN or Inf values. Add runtime checks for these, especially in the first few layers.
Mitigation Strategy
Build a validation pipeline comparing quantized vs original outputs. Set thresholds for acceptable divergence (typically less than 1% accuracy drop). Monitor continuously for drift.