Why Choose the FP16 Model in Weight Compression Using Optimum Intel / Neural Network Compression Framework (NNCF)?

Unable to determine the reason for choosing FP16 model in Weight Compression using Optimum Intel / NNCF.

FP16 precision is commonly used because it reduces model size by approximately 50% compared to FP32 while typically maintaining similar accuracy.
Using FP16 can reduce memory consumption and improve deployment efficiency on supported hardware. For many models, the accuracy impact is minimal compared to FP32.
Depending on the model and hardware platform, additional weight compression methods such as INT8 or INT4 compression may provide greater memory savings while maintaining acceptable accuracy.

Refer to OpenVINO™ NNCF Weight Compression documentation for the latest compression methods and deployment recommendations.
Refer to Optimum Intel documentation for OpenVINO™ model optimization workflows.