# Is (Selective) Round-To-Nearest Quantization All You Need? Source: [Is (Selective) Round-To-Nearest Quantization All You Need?]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:45:08.569Z Authors: Alex Kogan Published: 2025-05-21T18:01:32.000Z ## Abstract Quantization became a necessary tool for serving ever-increasing Large Language Models (LLMs). RTN (Round-to-Nearest) is perhaps the simplest quantization technique that has been around well before LLMs surged to the forefront of machine learning (ML) research. Yet, it has been largely dismissed by recent and more advanced quantization methods that claim superiority over RTN in nearly every aspect of performance. This work aims to dispel this established point of view, showing that RTN is not only much cheaper to apply, but also its token generation throughput can be better than and accuracy can be similar to more advanced alternatives. In particular, we discuss our implementation of RTN based on the recent Marlin kernels and demonstrate how the accuracy of RTN can be gradually improved by selectively increasing the data precision format of certain model layers and modules. Based on our results, we argue that RTN presents a viable and practical choice for quantizing LLMs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Medical Image Segmentation on AbdomenCT-1K (test)](): Medical Image Segmentation; AbdomenCT-1K (test); DSC: 93.499. ## Original sources and implementation [Original paper]() [PDF]()