Part 15
Completed
Quantize a Model
Implemented post-training quantization (PTQ) and quantization-aware training (QAT) for transformers. Explored INT8, INT4, and mixed-precision schemes. Implemented GPTQ, AWQ, and SmoothQuant methods with per-layer and per-channel scaling.
What I Built
Implemented post-training quantization (PTQ) and quantization-aware training (QAT) for transformers. Explored INT8, INT4, and mixed-precision schemes. Implemented GPTQ, AWQ, and SmoothQuant methods with per-layer and per-channel scaling.
Key Concepts
Post-Training QuantizationQuantization-Aware TrainingINT8INT4GPTQAWQSmoothQuantMixed Precision
Architecture
1
Quantization Engine2
GPTQ Implementer3
AWQ Adapter4
SmoothQuant Scaler5
Precision ManagerResults
INT8 quantization achieves 99% of FP16 quality with 2x speedup. INT4 (GPTQ) achieves 95% quality with 4x speedup. AWQ protects critical weights effectively.
Key Learnings
- Not all weights are equally important—protecting outliers is crucial
- Activation quantization is harder than weight quantization
- Per-channel scaling significantly improves quantization quality
Challenges
- Handling activation outliers that dominate quantization error
- Balancing speedup with quality degradation
- Implementing efficient INT4 kernels