QTIP: Quantization with Trellises and Incoherence Processing
This addresses the memory-bound inference issue in LLMs by improving PTQ methods, though it appears incremental as it builds on existing VQ-based approaches.
The paper tackles the problem of post-training quantization (PTQ) for LLMs by introducing QTIP, which uses trellis coded quantization (TCQ) to enable ultra-high-dimensional quantization, achieving state-of-the-art results in both quantization quality and inference speed.
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.