0
-1.0
Value Range: -1.0 to +1.0
INT4: 4-bit Integer
INT8: 8-bit Integer
FP32: 32-bit Float
✓ High Precision
PROS
INT4: 16 levels
INT8: 256 levels
FP32: ~4.3B levels
Discretization
+1.0
✓ Balanced Speed/Acc
✗ Edge Unfriendly
✗ High Bandwidth
✗ Slow Inference
✗ Large Memory (4x)
CONS
✓ Best for Training
✓ No Quantization Error
✓ Wide Dynamic Range
✓ 8x Memory Savings
✓ 4x Memory Savings
✗ Limited to Inference
✗ Hardware Dependent
✗ Calibration Required
✗ Minor Accuracy Drop
✓ Edge Deployment Ready
✓ TensorRT Optimized
✓ Lowest Power
✓ Fastest Inference
FP32 → INT8 → INT4
Model Quantization Comparison
✗ Not for All Models
✗ Complex Calibration
✗ Limited Hardware Support
✗ Significant Accuracy Loss
✓ Extreme Compression
Speed ↑ | Memory ↓ | Accuracy ↓
High Precision
Medium Precision
Low Precision
INT8 Tensor (8-bit)
FP32 Tensor (32-bit)
Quantization
INT4 ensor (4-bit)
