: These sensitive weights (usually less than 1% of the total) are extracted and stored in their original 16-bit precision.

: It is the first method to allow 3-4 bit quantization with almost no measurable loss in perplexity compared to the 16-bit baseline.

: It uses a Hessian-based regularizer to identify which weights are most sensitive to quantization.

: The final model is a combination of a dense, low-bit matrix and a sparse, high-precision matrix. 3. Key Performance Metrics