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What is model quantization? Smaller, faster LLMs

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. ....

Microsoft Research , Chinese Academy Of Sciences , Tensorflow Lite , Coral Edge , Chinese Academy , All Large Language Models ,

From a Lossless (~1.5:1) Compression Algorithm for Llama2 7B Weights to Variable Precision, Variable Range, Compressed Numeric Data Types for CNNs and LLMs

This paper attempts to address and reconcile two different issues: the existence of multiple numerical data formats (such as int8, bfloat16, fp8, etc., often non optimal for the application and not directly compatible with one another) and the necessity to reduce their bandwidth requirements, especially in the case of power hungry and slow DRAM. ....

Tim Dettmers , Ruslan Svirschevski , Tim Dettimers , Jarek Duka , Furu Wei , Alexander Borzunov , Shuming Ma , John Gustafson , Vincenzo Liguori , Zhongyu Wang , Artix Ultrascale , Kintex Ultrascale , Eric Winsor , Beren Millidge , Foundation Models , Connections For Efficient Neural Networks , Ocean Logic Pty Ltd , Ocean Logic Pty , Large Language Model , Asymmetrical Numeral Systems , Flexible Numerical Data , Bit Layer Multiply Accumulator , Inference Example , Weight Compression , Open Foundation , Their Own Game ,