@inproceedings{
tseng2024qtip,
title={{QTIP}: Quantization with Trellises and Incoherence Processing},
author={Albert Tseng and Qingyao Sun and David Hou and Christopher De Sa},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7sdkLVuYCU}
}
Post-training quantization (PTQ) reduces the memory footprint of LLMs by quantizing weights to low-precision datatypes.
Since LLM inference is usually memory-bound, PTQ methods can improve inference throughput.
Recent state-of-the-art PTQ approaches use vector quantization (VQ) to quantize multiple weights at once, which improves information utilization through better shaping.
However, VQ requires a codebook with size exponential in the dimension.
This limits current VQ-based PTQ works to low VQ dimensions ($\le 8$) that in turn limit quantization quality.
Here, we introduce QTIP, which instead uses trellis coded quantization (TCQ) to achieve ultra-high-dimensional quantization. 
TCQ uses a stateful decoder that separates the codebook size from the bitrate and effective dimension. 
QTIP introduces a spectrum of lookup-only to computed lookup-free trellis codes designed for a hardware-efficient "bitshift" trellis structure; these codes achieve state-of-the-art results in both quantization quality and inference speed.
QTIP: Quantization with Trellises and Incoherence Processing
Albert Tseng Qingyao Sun David Hou Christopher De Sa