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The recent rise of large language models (LLMs) has resulted in increasedefforts towards running LLMs at reduced precision. Running LLMs at lowerprecision supports resource constraints and furthers their democratization,enabling users to run billion-parameter LLMs on their personal devices. Tosupplement this ongoing effort, we propose INT-FP-QSim: an open-sourcesimulator that enables flexible evaluation of LLMs and vision transformers atvarious numerical precisions and formats. INT-FP-QSim leverages existingopen-source repositories such as TensorRT, QPytorch and AIMET for a combinedsimulator that supports various floating point and integer formats. With thehelp of our simulator, we survey the impact of different numerical formats onthe performance of LLMs and vision transformers at 4-bit weights and 4-bit or8-bit activations. We also compare recently proposed methods like AdaptiveBlock Floating Point, SmoothQuant, GPTQ and RPTQ on the model performances. Wehope INT-FP-QSim will enable researchers to flexibly simulate models at variousprecisions to support further research in quantization of LLMs and visiontransformers.

INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers