A new method called G^2PTQ improves post-training quantization (PTQ) for large language models by generalizing gradient compensation, addressing two complementary limitations in GPTQ-based methods that have become the de facto standard for reducing LLM memory and computational footprint without retraining. The approach targets the local, layer-wise objective used by existing GPTQ-based techniques.
Post-training quantization (PTQ) is a practical approach to reducing the memory and computational footprint of large language models (LLMs) without retraining. GPTQ-based methods have become the de facto standard, yet they suffer from two complementary limitations. Methods with local, layer-wise obj
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