MosaicQuant: Inlier-Outlier Disaggregation for Unified 4-Bit LLM Quantization

• URL: http://arxiv.org/abs/2606.15652v1
• Date: Sun, 14 Jun 2026 07:43:43 GMT
• Title: MosaicQuant: Inlier-Outlier Disaggregation for Unified 4-Bit LLM Quantization
• Abstract summary: We propose a unified 4-bit quantization paradigm for large language models (LLMs)<n>MosaicQuant quantizes the full weight matrix into a dense 4-bit base component, where inliers are captured faithfully while outliers are inevitably quantized.<n>Experiments on LLaMA3 and Qwen3 demonstrate that MosaicQuant preserves near-FP16 accuracy while achieving up to $1.24times$ speedup over the W16A16 baseline.
• Score: 27.433770037419624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 4-bit quantization significantly reduces the memory footprint and accelerates the inference of large language models (LLMs). However, its limited bit-width representation struggles to faithfully capture both dense common values (\emph{inliers}) and rare large-magnitude values (\emph{outliers}), causing substantial accuracy degradation. Existing mixed-precision methods mitigate this by retaining outliers in high precision, but at the cost of breaking the uniformity of low-bit execution, introducing precision conversion and extra data movement that undermine practical speedup. We propose \textbf{MosaicQuant}, a unified 4-bit LLM quantization paradigm built on a novel principle of \emph{inlier--outlier disaggregation}. Rather than elevating outlier precision, MosaicQuant quantizes the full weight matrix into a dense 4-bit base component, where inliers are captured faithfully while outlier are inevitably quantized. A sparse 4-bit residual component is then introduced to compensate for these quantization errors, selectively targeting the most error-critical weight blocks where output distortion is shown to be concentrated. However, a unified representation alone is insufficient, as naïvely executing the sparse residual as a separate kernel still breaks the unified low-bit inference pipeline. To bridge this gap, we introduce \textbf{ZipperEngine}, which fuses sparse block computation into the dense 4-bit GEMM kernel via an overlapped pipeline, unifying not only the representation but also the execution into a single coherent low-bit inference pipeline. Extensive experiments on LLaMA3 and Qwen3 demonstrate that MosaicQuant preserves near-FP16 accuracy while achieving up to $1.24\times$ speedup over the W16A16 baseline.

Related papers

• Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLM [0.0]
  Minima: NVFP4 W4A4 on all 496 linear layers, GDN included.<n>A four-part mechanism study explains why NVFP4's 16-element block scaling localizes the residual stream's extreme outliers.<n>A mechanistic account of why the recurrent half of a hybrid LLM is the easy half to quantize.
  arXiv  Detail & Related papers  (2026-09-03T17:04:26Z)
• ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs [4.431548809730958]
  ARCQuant is a framework that boosts NVFP4 performance via Augmented Residual Channels.<n>We show that ARCQuant achieves state-of-the-art accuracy, comparable to full-precision baselines in perplexity and downstream tasks.
  arXiv  Detail & Related papers  (2026-01-12T12:27:22Z)
• Post-Training Quantization via Residual Truncation and Zero Suppression for Diffusion Models [10.000323762676633]
  Diffusion models achieve high-quality image generation but face deployment challenges due to their high computational requirements.<n>We propose Quantization via Residual Truncation and Zero Suppression (QuaRTZ), a 4-bit PTQ scheme for diffusion models.<n>Our approach reduces rounding errors and improves quantization efficiency by balancing outlier preservation and LSB precision.
  arXiv  Detail & Related papers  (2025-09-30T15:55:42Z)
• Improving Block-Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations [22.127873567034825]
  Large language models (LLMs) demand extensive memory capacity during both fine-tuning and inference.<n>Existing methods apply block-wise quantization techniques, such as NF4 and AF4, to the network weights.<n>We show that these quantization techniques incur suboptimal quantization errors.
  arXiv  Detail & Related papers  (2025-05-10T14:00:15Z)
• ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantization [73.60493264901359]
  We present a unified framework for rigorous comparisons across 1-bit, 1.58-bit, 2-bit, 3-bit, and 4-bit quantization settings.<n>We show that ternary, 2-bit, and 3-bit quantization maintains comparable performance in the size-accuracy trade-off.<n>Considering hardware constraints, 2-bit quantization offers promising potential for memory reduction and speedup.
  arXiv  Detail & Related papers  (2025-02-04T18:59:26Z)
• ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals [10.860081994662645]
  Post-training quantization of large language models (LLMs) holds the promise in reducing the prohibitive computational cost at inference time.<n>We propose ResQ, a PTQ method that pushes further the state-of-the-art.<n>We demonstrate that ResQ outperforms recent uniform and mixed precision PTQ methods on a variety of benchmarks.
  arXiv  Detail & Related papers  (2024-12-18T22:01:55Z)
• AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference [6.699442219974261]
  AMXFP4 is a 4-bit asymmetric FP format that handles both issues using asymmetric shared scales.<n>AMXFP4 outperforms MXFP4 by 3% on VQA and exceeds rotation-based methods by 1.6% on CSQA.
  arXiv  Detail & Related papers  (2024-11-15T03:11:19Z)
• 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)
• SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models [63.118592279833656]
  Post-training quantization (PTQ) is an effective technique for compressing large language models (LLMs)<n>We propose SliM-LLM, a salience-driven mixed-precision quantization framework that allocates bit-widths at the group-wise.<n> Experiments show that SliM-LLM achieves superior performance across various LLMs at low bit-widths.
  arXiv  Detail & Related papers  (2024-05-23T16:21:48Z)
• DB-LLM: Accurate Dual-Binarization for Efficient LLMs [83.70686728471547]
  Large language models (LLMs) have significantly advanced the field of natural language processing. Existing ultra-low-bit quantization always causes severe accuracy drops. We propose a novel Dual-Binarization method for LLMs, namely DB-LLM.
  arXiv  Detail & Related papers  (2024-02-19T09:04:30Z)
• SqueezeLLM: Dense-and-Sparse Quantization [80.32162537942138]
  Main bottleneck for generative inference with LLMs is memory bandwidth, rather than compute, for single batch inference. We introduce SqueezeLLM, a post-training quantization framework that enables lossless compression to ultra-low precisions of up to 3-bit. Our framework incorporates two novel ideas: (i) sensitivity-based non-uniform quantization, which searches for the optimal bit precision assignment based on second-order information; and (ii) the Dense-and-Sparse decomposition that stores outliers and sensitive weight values in an efficient sparse format.
  arXiv  Detail & Related papers  (2023-06-13T08:57:54Z)
• HAWQV3: Dyadic Neural Network Quantization [73.11579145354801]
  Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values. We present HAWQV3, a novel mixed-precision integer-only quantization framework.
  arXiv  Detail & Related papers  (2020-11-20T23:51:43Z)

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.