AI RESEARCH
Leech Lattice Vector Quantization for Efficient LLM Compression
arXiv CS.LG
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ArXi:2603.11021v1 Announce Type: new Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds. While vector quantization (VQ) overcomes these limits by encoding blocks of parameters jointly, practical implementations must avoid the need for expensive lookup mechanisms or other explicit codebook storage. Lattice approaches address this through highly structured and dense packing.