LFQ: Logit-aware Final-block Quantization for Boosting the Generation Quality of Low-Bit Quantized LLMs
arXiv:2605.29756v1 Announce Type: new Abstract: As large language models continue to scale, low-bit weight-only post-training quantization (PTQ) offers a practical solution to their memory-efficient deployment. Although block-wise PTQ is capable of matching the full-precision (FP) baseline on basic language modeling and understanding, its quality is degraded for generative tasks -- especially at longer responses and extended chains of thought, which is critical in boosting task accuracy. We attribute this shortfall to two factors: (i) the omission of the unembedding layer (the LM head) in bloc
As LLMs continue to scale, the immediate need for efficient deployment, particularly in memory-constrained environments, is driving rapid innovation in quantization techniques.
This work addresses a critical bottleneck in deploying highly capable LLMs by improving their efficiency while maintaining generation quality, which is crucial for real-world applications and widespread adoption.
The proposed LFQ method promises to significantly enhance the generative performance of low-bit quantized LLMs, making more powerful models accessible for on-device or edge deployment without sacrificing critical task accuracy.
- · AI developers
- · On-device AI applications
- · Companies deploying large language models
- · Edge computing hardware manufacturers
- · Cloud-dependent LLM inference
Low-bit quantized LLMs will achieve higher performance in generative tasks, especially for longer, more complex outputs.
This improvement will accelerate the development and deployment of sophisticated AI agents and assistants on a wider range of hardware platforms.
Increased accessibility of advanced LLM capabilities could lead to more diversified and personalized AI applications, further decentralizing AI compute power.
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Read at arXiv cs.AI