arXiv:2606.04349v1 Announce Type: cross Abstract: Conventional Post-Training Quantization (PTQ) methods struggle with 4-bit Omni-modal Large Language Models (OLLMs) due to the extreme distribution heterogeneity and disparate outlier patterns across modalities. To address this, we propose MorphoQuant, a modality-aware PTQ framework engineered to preserve cross-modal morphology and mitigate outlier loss. Specifically, we introduce Distribution-Aware Bias Compensation (DABC), which selectively absorbs long-tailed outliers into channel-wise biases. This mechanism safeguards outlier magnitudes whil
The proliferation of Large Language Models (LLMs) and their expansion into multimodal capabilities necessitates efficient deployment strategies, making quantization research increasingly critical.
This development allows for more efficient deployment of complex OLLMs, reducing computational and energy costs, which is vital for wider adoption and edge computing scenarios.
Current limitations in quantizing OLLMs due to heterogeneous data distributions are being overcome, paving the way for more performant 4-bit OLLMs with reduced resource footprints.
- · AI hardware manufacturers
- · Edge AI developers
- · Cloud AI providers (cost savings)
- · AI researchers
- · Companies reliant on high-precision, unoptimised OLLMs
More powerful and efficient OLLM deployments become feasible across various computational environments.
Increased accessibility and lower operational costs for advanced AI could accelerate the development and deployment of AI agents and complex autonomous systems.
The reduced compute burden might alleviate some pressure on energy and compute supply chains, although overall demand for AI will likely continue to grow.
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Read at arXiv cs.AI