Choosing Quantization Strategy: Decision Framework
Choosing the right quantization strategy depends on your constraints. This decision framework helps you pick the optimal approach for your situation.
Start With Your Constraints
If you cannot afford ANY accuracy loss, keep FP16. Full precision is your baseline. If you need fastest deployment with minimal work, try post-training quantization (PTQ) first. It requires no retraining and works in hours.
If PTQ accuracy is unacceptable (more than 2% drop), move to quantization-aware training (QAT). Budget 2-3x your original training time. If deploying to edge devices with hard memory limits, target INT8 or INT4 with aggressive pruning.
Model Type Considerations
CNNs quantize easily. Most convolutional networks achieve less than 1% accuracy loss with PTQ INT8. Vision transformers are harder - attention mechanisms are sensitive. Start with dynamic quantization.
Large language models benefit most from weight-only quantization (GPTQ, AWQ). This addresses the memory bottleneck while preserving quality. Target INT4 weights with FP16 activations for best balance.
Pro tip: Always benchmark on YOUR data. Published benchmarks rarely match real-world performance on domain-specific tasks.
Decision Checklist
1) Define acceptable accuracy loss. 2) Measure baseline latency and memory. 3) Try PTQ first (lowest effort). 4) If PTQ fails, invest in QAT. 5) Validate on production-like data. 6) Monitor drift after deployment.