MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

MorphoQuant is a novel Post-Training Quantization (PTQ) framework designed for Omni-modal Large Language Models (OLLMs), specifically addressing the challenges of extreme distribution heterogeneity and disparate outlier patterns in 4-bit OLLMs like Qwen2.5-Omni. The framework introduces Distribution-Aware Bias Compensation (DABC), which selectively absorbs long-tailed outliers into channel-wise biases. This mechanism safeguards outlier magnitudes while maintaining high-precision discretization for dense inliers across diverse modal distributions. Complementing DABC, Morphology-Directed Quantization Function Optimization (MDQFO) co-optimizes the quantization grid with the bias mask, ensuring fine-grained alignment across modalities. Extensive evaluations on Qwen2.5-Omni across benchmarks such as MMMU and Video-MME demonstrate MorphoQuant's superiority. Notably, its W4A4 model achieves 76.63% on ScienceQA, significantly outperforming leading W4A4 methods and surprisingly surpassing the W4A16 baseline, showcasing an exceptional accuracy-efficiency trade-off.

Key takeaway

For AI Engineers deploying Omni-modal Large Language Models on resource-constrained edge devices, MorphoQuant provides a critical solution. You can achieve W4A4 quantization with accuracy comparable to, or even surpassing, W4A16 baselines, significantly reducing memory and computational costs. Consider integrating modality-aware bias compensation and morphology-directed optimization to overcome cross-modal distribution challenges and enable efficient OLLM deployment.

Key insights

Modality-aware quantization with bias compensation and morphology-directed optimization significantly improves 4-bit OLLM performance.

Principles

• Outlier distribution varies across modalities.
• Decoupling outliers from inliers is crucial.
• Preserving feature morphology is key for reasoning fidelity.

Method

MorphoQuant applies Distribution-Aware Bias Compensation (DABC) to absorb outliers into channel-wise biases, then uses Morphology-Directed Quantization Function Optimization (MDQFO) with a composite loss to refine the quantization grid.

In practice

• Use dispersion scores to identify outlier channels.
• Fold truncation residuals into bias terms.
• Co-optimize quantization with a morphology-focused loss.

Topics

• Omni-modal LLMs
• Post-Training Quantization
• 4-bit Quantization
• Distribution-Aware Bias Compensation
• Morphology-Directed Optimization
• Qwen2.5-Omni

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization — HARP adaptively rotates LLM weights for extreme low-bit quantization, improving accuracy and inference speed over fixed methods.
• ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models — ActQuant enables sub-4-bit quantization for VLA models by action-guided mixed-precision allocation and efficient C/C++ deployment.
• Qwen3.5-Omni is here! Scaling up to a Native Omni-modal AGI — Alibaba's Qwen3.5-Omni unifies diverse modalities and advanced conversational features into a single, highly capable AI system.
• A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models — KL divergence effectively identifies quantization sensitivity in hybrid SSM-Transformer models for efficient edge deployment.
• Advanced MXFP4 Quantization: Combining Fine-Tuned Rotations with SmoothQuant for Near-Lossless Compression — Combining fine-tuned online rotations and SmoothQuant scales significantly improves MXFP4 quantization accuracy for language models.
• Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats — HiFloat formats, particularly HiF4, offer robust low-bit quantization for LLM inference on Ascend NPUs, especially at 4-bit precision.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.