FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression
Compressing large language models (LLMs) for deployment on commodity GPUs remains challenging: conventional scalar quantization is limited to fixed bit-widths (e.g., 8/4/3-bit), offers only a few discrete compression points, and typically requires calibration data. We present FASQ (Flexible Accelerated Subspace Quantization), a calibration-free framework that applies product quantization to LLM weight matrices. By tuning two parameters, sub-vector size and codebook cardinality, FASQ exposes a continuous design space spanning 27-49% of the original FP16 model size, filling compression gaps that fixed-bit schemes cannot reach. On Meta-Llama-3-8B, FASQ surpasses 4-bit GPTQ and AWQ in accurac...
| Authors | Ye Qiao, Yian Wang, Zhiheng Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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