Huawei's Zurich Lab unveils SINQ, an open-source quantization method that it claims can reduce LLM memory use by 60-70% without significant quality loss (Carl Franzen/VentureBeat)

Carl Franzen / VentureBeat: Huawei's Zurich Lab unveils SINQ, an open-source quantization method that it claims can reduce LLM memory use by 60-70% without significant quality loss  —  - Dual-Axis Scaling: Instead of using a single scale factor for quantizing a matrix, SINQ uses separate scaling vectors for rows and columns.

Oct 4, 2025 - 18:00
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Huawei's Zurich Lab unveils SINQ, an open-source quantization method that it claims can reduce LLM memory use by 60-70% without significant quality loss (Carl Franzen/VentureBeat)

Carl Franzen / VentureBeat:
Huawei's Zurich Lab unveils SINQ, an open-source quantization method that it claims can reduce LLM memory use by 60-70% without significant quality loss  —  - Dual-Axis Scaling: Instead of using a single scale factor for quantizing a matrix, SINQ uses separate scaling vectors for rows and columns.

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