Research

RUQuant: Towards Refining Uniform Quantization for Large Language Models

RUQuant advances uniform quantization techniques to slash LLM memory overhead, enabling deployment on resource-constrained hardware without major performance loss.

Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline

RUQuant proposes refined uniform quantization techniques for large language models. Quantization is a critical approach for reducing model size and memory footprint, enabling efficient deployment across resource-constrained environments. This research directly addresses practical bottlenecks in LLM deployment.

Read original at arXiv CS.CL (Computation & Language)

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