Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data
arXiv:2606.04238v1 Announce Type: new Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typically incurs severe accuracy degradation. These gains are particularly relevant for edge and on-device deployment, where memory capacity and bandwidth are primary constraints. In this work, we extend Recover-LoRA -- a lightweight, data-free accuracy recovery method originally developed for general model weight corruption -- to the setting of ultra-low-bit quantization. We propose a selective mixed-prec
The increasing scale and deployment demands of large language models necessitate breakthroughs in efficient inference, especially for edge devices.
Improving the efficiency of LLMs via aggressive quantization directly addresses the compute and energy bottlenecks limiting wider AI deployment.
Previously intractable 2-bit quantization for LLMs, which provides significant memory and throughput gains, becomes more viable for practical applications without severe accuracy loss.
- · Edge device manufacturers
- · LLM developers
- · AI-powered mobile applications
- · On-device AI chipmakers
- · High-end cloud GPU providers (for certain use cases)
- · Companies reliant solely on massive server-side LLM inference
Wider deployment of high-performance LLMs on power-constrained and memory-limited devices like smartphones and embedded systems.
Accelerated development of new applications and services that leverage localized, efficient AI at the 'edge', reducing reliance on constant cloud connectivity.
Increased competition among hardware manufacturers to integrate these optimized LLMs, potentially decentralizing AI processing and reducing the dominance of centralized compute resources.
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Read at arXiv cs.LG