AI RESEARCH
Statistically-Lossless Quantization of Large Language Models
arXiv CS.LG
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ArXi:2605.02404v1 Announce Type: new Model quantization has become essential for efficient large language model deployment, yet existing approaches involve clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques preserve fidelity but typically do not accelerate inference. This paper explores the middle ground of statistically-lossless compression through three complementary notions of losslessness for quantized LLMs.