Quantization precision
The numerical representation precision of model weights in machine learning, commonly FP16, INT8, INT4 and others, which affects model memory footprint and inference speed.
Core Facts
Timeline (last 90 days)
Quantization precision directly affects memory usage and image quality: 4-bit is small in size and suitable for devices with limited memory, 8-bit has higher precision, and FP16 is close to original precision but has the highest hardware requirements.
Quantization compresses the storage precision of model parameters from 16-bit down to 8-bit or even 4-bit, thereby reducing memory usage, but at the cost of potential loss in model accuracy.
All Facts (2)
Quantization compresses the storage precision of model parameters from 16-bit down to 8-bit or even 4-bit, thereby reducing memory usage, but at the cost of potential loss in model accuracy.
65%UnverifiedQuantization precision directly affects memory usage and image quality: 4-bit is small in size and suitable for devices with limited memory, 8-bit has higher precision, and FP16 is close to original precision but has the highest hardware requirements.
50%