Impact of Quantization on Different Tasks
| Task Type | Quantization Sensitivity | Typical Impact |
|---|---|---|
| Vision tasks | Relatively low | 8-bit almost lossless, 4-bit+QAT can recover 98%+ performance |
| Language tasks | Medium | Logical reasoning tasks more sensitive to precision |
| Cross-modal tasks | Relatively high | Affected by superposition of vision and language errors |
Quantization Accuracy Comparison
- 8-bit quantization: Performance loss <1% in most cases, can be directly deployed
- 4-bit quantization: Basic scheme may cause 3-10% degradation, can be controlled at 1-3% through QAT, mixed precision and other technologies
Mainstream Optimization Technologies
- Quantization-Aware Training (QAT): Simulates quantization effects during training, 4-bit quantization accuracy loss can be reduced to within 1%
- Mixed precision quantization: Sensitive layers maintain 8-bit, other layers use 4-bit
- Per-channel quantization: Different channels use different quantization parameters
- Advanced calibration methods: Post-training quantization technologies like GPTQ, AWQ
Model Scale Impact
- Large models (>10B parameters) are more robust to quantization, 4-bit quantization can maintain 96%+ performance
- Small models (<1B parameters) are more significantly affected, recommend 6-8bit conservative scheme
Application Suggestions
- Edge devices: Recommend 4-bit mixed precision quantization
- Cloud inference: Can use 8-bit to maintain best accuracy
- High real-time requirement scenarios: 4-bit can provide 2-4x speedup
Summary
With advances in quantization technology, modern Transformer architectures show good robustness to quantization. In complex reasoning tasks, it is recommended to maintain 6-8bit precision; perception tasks (like image classification) are basically lossless with 4-bit and below quantization.