PolarQuant: Optimal Gaussian Weight Quantization via Hadamard Rotation for LLM Compression

• URL: http://arxiv.org/abs/2603.29078v1
• Date: Mon, 30 Mar 2026 23:33:28 GMT
• Title: PolarQuant: Optimal Gaussian Weight Quantization via Hadamard Rotation for LLM Compression
• Abstract summary: PolarQuant is a post-training weight quantization method for large language models.<n>It exploits the distributional structure of neural network weights to achieve near-lossless compression.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present PolarQuant, a post-training weight quantization method for large language models (LLMs) that exploits the distributional structure of neural network weights to achieve near-lossless compression. PolarQuant operates in three stages: (1) block-wise normalization to the unit hypersphere, (2) Walsh-Hadamard rotation to transform coordinates into approximately Gaussian random variables, and (3) quantization with centroids matched to the Gaussian distribution. Our ablation reveals that Hadamard rotation alone accounts for 98% of the quality improvement, reducing Qwen3.5-9B perplexity from 6.90 (absmax Q5) to 6.40 (Delta = +0.03 from FP16), making it practically lossless without any calibration data. Furthermore, PolarQuant functions as an effective preprocessing step for downstream INT4 quantizers: PolarQuant Q5 dequantized and re-quantized by torchao INT4 achieves perplexity 6.56 versus 6.68 for direct absmax INT4, while maintaining 43.1 tok/s throughput at 6.5 GB VRAM. Code and models are publicly available.

Related papers

• HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models [0.0]
  HyperQuant is a unified post-training quantization pipeline for the weights and the KV cache of large language and diffusion transformers.<n>It outperforms the recent HIGGS scheme at every operating point from 3 to 5 bits per scalar (bps) on weights, and beats both TurboQuant and OCTOPUS on KV quantization down to 1.7 bps.<n>It compresses the linear weights 3.9x and the KV cache 3.79x at near-lossless quality.
  arXiv  Detail & Related papers  (2026-06-22T14:30:47Z)
• ITQ3_S: High-Fidelity 3-bit LLM Inference via Interleaved Ternary Quantization with Rotation-Domain Smoothing [0.0]
  We present ITQ3_S (Interleaved Ternary Quantization -- Specialized), a novel 3-bit weight quantization format for LLMs integrating TurboQuant (TQ)<n>Conventional 3-bit methods suffer precision loss from heavy-tailed weight distributions and inter-channel outliers.<n>ITQ3_S pre-rotates the weight space via FWHT before quantization, spreading energy across the vector and inducing a near-Gaussian distribution.
  arXiv  Detail & Related papers  (2026-03-30T00:03:22Z)
• BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models [56.504879072674015]
  We propose Bit-Plane Decomposition Quantization (BPDQ), which constructs a variable quantization grid via bit-planes and scalar coefficients.<n>BPDQ enables serving Qwen2.5-72B on a single GTX 3090 with 83.85% GSM8K accuracy (vs. 90.83% at 16-bit)
  arXiv  Detail & Related papers  (2026-02-04T02:54:37Z)
• Phase Estimation with Compressed Controlled Time Evolution [0.0]
  This work establishes a compression protocol for encoding the controlled time evolution operator of translationally invariant, local Hamiltonians into a quantum circuit.<n>It achieves a near-optimal scaling in circuit depth while reducing the control overhead from a multiplicative to an additive factor.
  arXiv  Detail & Related papers  (2025-11-26T09:54:09Z)
• KurTail : Kurtosis-based LLM Quantization [51.24081396305435]
  KurTail is a new post-training quantization scheme that mitigates outliers in the activations of large language models.<n>It offers a 13.3% boost in MMLU accuracy and a 15.5% drop in Wiki perplexity compared to QuaRot.<n>It also outperforms SpinQuant with a 2.6% MMLU gain and reduces perplexity by 2.9%, all while reducing the training cost.
  arXiv  Detail & Related papers  (2025-03-03T12:43:06Z)
• SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models [61.474101404805545]
  Diffusion models can generate high-quality images, but as they scale, rising memory demands and higher latency pose deployment challenges.<n>We propose SVDQuant, a new 4-bit quantization paradigm to overcome this limitation.<n>We reduce the memory usage for the 12B FLUX.1 models by 3.5$times$, achieving 3.0$times$ speedup over the 4-bit weight-only quantization (W4A16) baseline.
  arXiv  Detail & Related papers  (2024-11-07T18:59:58Z)
• FlatQuant: Flatness Matters for LLM Quantization [58.28221892035609]
  We propose FlatQuant, a new post-training quantization approach that enhances the flatness of weights and activations.<n>Our approach identifies optimal affine transformations for each linear layer, calibrated in hours via a lightweight objective.<n>It achieves less than 1% accuracy drop for W4A4 quantization on the LLaMA-3-70B model, surpassing SpinQuant by 7.5%.
  arXiv  Detail & Related papers  (2024-10-12T08:10:28Z)
• SpinQuant: LLM quantization with learned rotations [49.07335692298487]
  Post-training quantization (PTQ) techniques applied to weights, activations, and the KV cache greatly reduce memory usage, latency, and power consumption of Large Language Models (LLMs)<n>We identify a collection of applicable rotation parameterizations that lead to identical outputs in full-precision Transformer architectures while enhancing quantization accuracy.<n>We propose SpinQuant, a novel approach that incorporates learned rotation matrices for optimal quantized network accuracy.
  arXiv  Detail & Related papers  (2024-05-26T02:15:49Z)
• KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization [67.74400574357472]
  LLMs are seeing growing use for applications which require large context windows, and with these large context windows KV cache activations surface as the dominant contributor to memory consumption during inference.<n> Quantization is a promising approach for compressing KV cache activations; however, existing solutions fail to represent activations accurately in sub-4-bit precision.<n>Our work, KVQuant, facilitates low precision KV cache quantization by incorporating several novel methods.
  arXiv  Detail & Related papers  (2024-01-31T18:58:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.