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Quantization has emerged as one of the primary techniques for reducing the memory footprint of large language models during inference. A prominent example is Quantized Johnson$-$Lindenstrauss (QJL), which compresses KV caches using 1-bit asymmetric quantization while approximately preserving the inner products required...
This paper explores the generalized problem of (approximately) computing the largest output entries, with an approximation error dependent solely on the smaller entries, from the viewpoint of sparse recovery, and shows that any sparse matrix multiplication algorithm with running time T(n, m_{in}, m_{out}) can be transf...
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