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RDQ: Residual Distribution Quantization for Large Language Models
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Research identifies residual stream distributional drift as the root cause of sharp degradation in LLM post-training quantization below 4-bit precision.
OneBench interpretation
Institutional assessment
So what
Addressing quantization noise is critical for deploying smaller, more cost-effective LLMs in bank-grade inference environments.
Do what
This research could lead to more efficient, smaller LLMs suitable for on-premises deployment, shifting the build-vs-buy calculus for specific use cases.