SPEAR: A System for Post-Quantization Error-Adaptive Recovery Enabling Efficient Low-Bit LLM Serving
arXiv:2606.11244v1 Announce Type: cross Abstract: Efficient large language model (LLM) serving is increasingly constrained by deployment cost. Quantization is a key technique for reducing serving cost, yet even state-of-the-art 4-bit quantizers exhibit a noticeable quality gap from FP16, particularly for smaller models where low-bit serving is most beneficial. We identify a fundamental cause of this gap: quantization error is highly input-dependent and varies substantially across tokens, while existing post-quantization compensation methods are static and apply identical corrections to all inp
The increasing scale of LLMs and the corresponding computational demands are driving the urgent need for more efficient serving techniques.
This development directly addresses a key constraint in deploying large language models, making advanced AI more accessible and cost-effective.
New post-quantization recovery methods tailored to input-dependent errors will significantly improve the quality of low-bit LLM serving, bridging the gap with higher precision models.
- · AI cloud providers
- · LLM developers
- · Companies deploying AI at scale
- · Edge AI hardware manufacturers
- · Companies solely relying on high-precision LLM architectures
More widespread and cheaper deployment of powerful LLMs will accelerate AI integration across industries.
Reduced operational costs for AI could stimulate further innovation in model architectures and applications, especially for smaller models.
Increased accessibility of advanced AI might lead to a greater democratization of AI development and research beyond well-funded institutions.
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Read at arXiv cs.AI