post-training-quantization

This paper revisits Kashin-decomposition-based weight quantization for large language models and proposes an improved algorithm using structured orthogonal transforms, reducing computational cost and ensuring numerical stability compared to methods like OPTQ and QuIP.

This research report evaluates post-training ternarization of the Qwen3-4B model, achieving a 1.641-bit effective weight representation with substantial storage compression, while noting a performance trade-off and unresolved deployment acceleration issues.

QTEA is a sub-2-bit post-training quantization framework for large language models that achieves ternary weight compression with improved accuracy and hardware efficiency through sparse residual salient weights and column-wise optimization.

The paper proposes REAL-Q, a novel end-to-end aligned post-training quantization method for large language models that uses dynamic gradient descent to mitigate quantization errors, achieving significant improvements in KL divergence reduction over state-of-the-art methods.

OCGQuant proposes an outlier-companion grouping method for NVFP4 quantization to reduce activation block errors, enhancing LLM inference efficiency. Experiments on Llama3 and Qwen3 demonstrate its superior performance over existing post-training quantization techniques.

HyGenQ is a post-training quantization framework for hybrid iterative generative models that addresses challenges like excessive outliers and amplified anomalies, enabling 8-bit precision quantization while maintaining generation quality.

ReRound is a post-training quantization method that uses a conditional diffusion model to guide rounding of near-midpoint weights, improving small LLM accuracy at 3-bit and 4-bit quantization without inference overhead.

ReQuant introduces a backpropagation-free, fixed-grid discrete refinement stage for post-training quantization (PTQ) that iteratively improves initial quantized models while preserving the quantized format, showing consistent gains across various LLMs and bit-widths.

Introduces Recurrent Residual Quantization (RRQ), a post-training quantization framework that enables multiple effective precisions from a single LLM checkpoint via additive residual refinement, improving flexibility and construction speed.

ExTernD introduces an expanded-rank ternary decomposition for post-training LLM quantization, enabling accuracy approaching bf16 by using a factored representation with free inner rank. It matches Q4_K accuracy at 5.2-5.5 effective bits per weight on models like Gemma-4 and Qwen3.5.

KronQ is a post-training quantization framework that incorporates gradient covariance using a Kronecker-factored Hessian approximation, enabling bidirectional incoherence processing and improved sensitivity metrics for mixed-precision allocation. It achieves low perplexity even at 2-bit weight-only quantization on large models like LLaMA-3-70B.

OrbitQuant introduces a data-agnostic quantization method for diffusion transformers that eliminates the need for recalibration across timesteps and modalities, achieving state-of-the-art post-training quantization at low-bit settings for models like FLUX.1 and CogVideoX.

CAT-Q introduces a post-training ternary quantization method for LLMs that uses learnable modulation and softened ternarization, achieving superior performance over BitNet 1.58-bit while using only 512 calibration samples and scaling to 235B parameters.

A novel end-to-end framework for LLM compression that jointly optimizes structural pruning and mixed-precision quantization, achieving significant perplexity reductions and speedups over state-of-the-art methods, especially at ultra-low bit precisions.

ScaleSweep proposes a new block scale initialization method for NVFP4 post-training quantization of LLMs, achieving improved accuracy by sweeping over feasible block scale candidates. Experiments on Llama and Qwen models show it preserves over 93% of full-precision performance under aggressive quantization.

This paper proposes FAIR-Calib, a two-stage post-training quantization framework for diffusion large language models that addresses the instability of token commitments during iterative refinement. It achieves state-of-the-art results on LLaDA and Dream models under low-bit quantization.

The paper proposes VSRAQ, a post-training quantization method for Mixture-of-Experts models that preserves expert-selection behavior by aligning routing-relevant logits and expert ordering, reducing quantization-induced degradation without inference overhead.

This paper introduces Qift, a fixed no-zero two-bit weight quantization level set designed for Hadamard-rotated LLMs, achieving improved W2A4/KV4 inference by leveraging the near-zero-centered Gaussian-like distribution of rotated weights. Experiments on LLaMA-2-7B and LLaMA-3.1-8B show consistent perplexity gains over standard W2 quantization.

Introduces QAM-W, a joint 2D codebook quantization method for LLM weights using Hadamard rotation and activation-aware scaling, achieving near BF16 perplexity at 5–6 bits per weight and matching SmoothQuant W8A8 quality with 32% fewer weight bits.

This paper presents Tail-Aware HiFloat4, a W4A4 post-training quantization method for the Wan2.2 text-to-video diffusion model, which uses activation-tail-aware percentile calibration to mitigate outlier effects while preserving HiFloat4 arithmetic.