Advancing Model Refinement: Muon-Optimized Distillation and Quantization for LLM Deployment

2026-01-14

AI Summary

This work addresses the challenges of deploying large language models (LLMs) on edge devices, where high computational demands, memory consumption, and energy costs hinder practicality, and where achieving an optimal trade-off between compression ratio and task performance remains difficult. To tackle this, the authors propose an integrated framework that synergistically combines GPTQ quantization, low-rank adaptation (LoRA), and task-oriented data distillation. Notably, they introduce the Muon optimizer into the quantization-aware fine-tuning pipeline for the first time, further enhanced by Bayesian hyperparameter optimization and KL divergence–driven knowledge distillation to enable joint optimization. The resulting approach achieves a 2× reduction in memory footprint—e.g., from 6 GB to 3 GB—while preserving model accuracy and significantly outperforming standard GPTQ baselines across established LLM benchmarks, thereby effectively mitigating performance degradation induced by quantization.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large Language Models (LLMs) enable advanced natural language processing but face deployment challenges on resource-constrained edge devices due to high computational, memory, and energy demands. Optimizing these models requires addressing three key challenges: acquiring task-specific data, fine-tuning for performance, and compressing models to accelerate inference while reducing resource demands. We propose an integrated framework combining GPTQ-based quantization, low-rank adaptation (LoRA), and a specialized data distillation process to significantly reduce model size and complexity while preserving or enhancing task-specific performance. By leveraging data distillation, knowledge distillation via Kullback-Leibler divergence, Bayesian hyperparameter optimization, and the Muon optimizer, our pipeline achieves up to 2x memory compression (e.g., reducing a 6GB model to 3GB) and enables efficient inference for specialized tasks. Empirical results demonstrate superior performance on standard LLM benchmarks compared to GPTQ quantization alone, with the Muon optimizer notably enhancing fine-tuned models'resistance to accuracy decay during quantization.

Problem

• Large Language Models
• model compression
• edge deployment
• quantization
• resource constraints

Innovation

• Muon optimizer
• GPTQ quantization
• knowledge distillation
• low-rank adaptation
• model compression