Author

Ke-Wei Zhang

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#artificial intelligence Preprint Oct 2026

SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability

Extreme low-bit weight-only quantization is an effective approach to reducing the deployment costs of large language models. Below 3 bits, scalar quantization often suffers severe accuracy loss, while vector quantization (VQ) better preserves accuracy through more expressive joint encoding. However, existing VQ methods...

Xiang-Long Yan, Ze-Nan Wu, Ke-Wei Zhang et al. · 0 citations

#small language model Preprint Sep 2026

SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models

SPHQuant is a rotation-free spherical weight-only quantization framework for VLMs that isolates outlier magnitude into the radius while keeping directions bounded and statistically regular and designs a hardware-friendly GEMV kernel that keeps the direction codebook small enough for shared-memory lookup and packs radia...

Ke-Wei Zhang, Zheng Chen, Hao-Tong Qin et al. · 1 citation · ⚡1