AE-LLM: Adaptive Efficiency Optimization for Large Language Models

• URL: http://arxiv.org/abs/2603.20492v1
• Date: Fri, 20 Mar 2026 20:46:18 GMT
• Title: AE-LLM: Adaptive Efficiency Optimization for Large Language Models
• Abstract summary: Large Language Models (LLMs) have achieved remarkable success across diverse applications, yet their deployment remains challenging due to substantial computational costs, memory requirements, and energy consumption.<n>Recent empirical studies have demonstrated that no single efficiency technique is universally optimal; instead, the effectiveness of methods such as efficient attention mechanisms, mixture-of-experts (MoE), parameter-efficient fine-tuning, and quantization varies significantly depending on task characteristics, resource constraints, and model scales.<n>We propose AE-LLM, a unified framework that automatically selects and combines optimal efficiency techniques tailored to specific deployment scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have achieved remarkable success across diverse applications, yet their deployment remains challenging due to substantial computational costs, memory requirements, and energy consumption. Recent empirical studies have demonstrated that no single efficiency technique is universally optimal; instead, the effectiveness of methods such as efficient attention mechanisms, mixture-of-experts (MoE), parameter-efficient fine-tuning, and quantization varies significantly depending on task characteristics, resource constraints, and model scales. Building upon these insights, we propose AE-LLM, a unified framework that automatically selects and combines optimal efficiency techniques tailored to specific deployment scenarios. Our approach introduces a multi-objective optimization framework that jointly considers accuracy, latency, memory footprint, and energy consumption, while accounting for hardware constraints and task requirements. We develop an efficient search algorithm that explores the combinatorial space of efficiency techniques across architecture, fine-tuning, and inference stages, identifying Pareto-optimal configurations. Extensive experiments across 15 models (0.5B-70B parameters) and 10 diverse tasks demonstrate that AE-LLM achieves an average of $2.8\times$ improvement in efficiency metrics while maintaining competitive accuracy (within 1.2\% of baseline), compared to static efficiency configurations. Furthermore, our framework generalizes effectively to vision-language models, achieving similar efficiency gains. Our contributions provide practitioners with an automated tool for navigating the complex trade-off landscape of LLM efficiency optimization.

Related papers

• Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation [61.72280804975896]
  Large Language Models (LLMs) are capable of generating syntactically correct and functionally complete programs, greatly streamlining software development.<n>Existing approaches to bridging this efficiency gap typically involve either iteratively optimizing code after generation or fine-tuning models on corpora of efficient code.<n>EffiSkel is an efficiency skeleton-guided framework that explicitly extracts and learns efficiency skeletons-abstract, reusable structural patterns efficient code.
  arXiv  Detail & Related papers  (2026-06-05T01:49:20Z)
• MO-SAE:Multi-Objective Stacked Autoencoders Optimization for Edge Anomaly Detection [4.037548309391426]
  Stacked AutoEncoders (SAE) have been widely adopted in edge anomaly detection scenarios.<n>But the resource-intensive nature of SAE can pose significant challenges for edge devices.<n>We propose an integrated optimization framework that jointly considers these critical factors to achieve balanced and adaptive system-level optimization.
  arXiv  Detail & Related papers  (2026-03-14T11:10:59Z)
• Software-Hardware Co-optimization for Modular E2E AV Paradigm: A Unified Framework of Optimization Approaches, Simulation Environment and Evaluation Metrics [21.03304462504213]
  This paper proposes a reusable software and hardware co-optimization and closed-loop evaluation framework for ME2E autonomous driving inference.<n>The proposed framework preserves baseline-level driving performance while significantly reducing inference latency and energy consumption.
  arXiv  Detail & Related papers  (2026-01-12T10:22:50Z)
• On the Scaling of Robustness and Effectiveness in Dense Retrieval [111.58315434849047]
  Robustness and effectiveness are critical aspects of developing dense retrieval models for real-world applications.<n>Recent work has addressed scaling laws of effectiveness in dense retrieval, revealing a power-law relationship between effectiveness and the size of models and data.<n>We find that robustness and effectiveness exhibit different scaling patterns, leading to significant resource costs when jointly improving both.
  arXiv  Detail & Related papers  (2025-05-30T06:57:27Z)
• Energy Considerations of Large Language Model Inference and Efficiency Optimizations [28.55549828393871]
  As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise.<n>We systematically analyze the energy implications of common inference efficiency optimizations across diverse NLP and AI workloads.<n>Our findings reveal that the proper application of relevant inference efficiency optimizations can reduce total energy use by up to 73% from unoptimized baselines.
  arXiv  Detail & Related papers  (2025-04-24T15:45:05Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving.<n>Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.<n>We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization [0.0]
  We introduce an efficient and robust auto-tuning framework for hyper parameter selection in dimension reduction (DR) algorithms. Our approach enables efficient hyper parameter selection with multi-objective trade-offs and allows us to perform data-driven analysis. We evaluate our results on various synthetic and real-world datasets using multiple quality metrics.
  arXiv  Detail & Related papers  (2023-06-01T05:36:22Z)
• Bayesian Optimization for Selecting Efficient Machine Learning Models [53.202224677485525]
  We present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency. Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency.
  arXiv  Detail & Related papers  (2020-08-02T02:56:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.