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arXiv · 2601.17187

High-Rate Quantized Matrix Multiplication I

Or Ordentlich·Yury Polyanskiy
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Abstract

This paper investigates the problem of quantized matrix multiplication (MatMul), which has become crucial for the efficient deployment of large language models (LLMs). We consider a Generic MatMul setting, where both matrices must be quantized (weight+activation quantization) without specific apriori (calibration) statistical information about the factors. We review the fundamental information-theoretic tradeoff between quantization rate and distortion (high-rate theory), and contrast those with the performance of popular quantization schemes (absmax INT and floating-point (FP)), for which we also derive accurate heuristic approximations. Part II of this paper studies the weight-only quantization setup where second-order statistics of the activation matrices are available at the encoder.

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Or Ordentlich, Yury Polyanskiy. 2026-01-23. High-Rate Quantized Matrix Multiplication I. https://arxiv.org/abs/2601.17187

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