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.
Source: infoworld.com
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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.
Source: design-reuse.com