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AE-LLM: Adaptive Efficiency Optimization for Large Language Models

Kaito Tanaka, Masato Ito, Yuji Nishimura, Keisuke Matsuda, Aya Nakayama

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.

AE-LLM: Adaptive Efficiency Optimization for Large Language Models

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 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.
Paper Structure (34 sections, 8 equations, 4 figures, 6 tables, 1 algorithm)

This paper contains 34 sections, 8 equations, 4 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Efficient Architecture Design.
  4. Parameter-Efficient Fine-Tuning and Reasoning.
  5. Model Quantization, Pruning, and Generation Efficiency.
  6. Efficiency and Multi-modal Benchmarks.
  7. Resource-Constrained Deployment.
  8. Methodology
  9. Problem Formulation
  10. Configuration Space Design
  11. Efficient Search Algorithm
  12. Phase 1: Predictive Performance Modeling
  13. Phase 2: Multi-Objective Evolutionary Search
  14. Algorithm Complexity Analysis
  15. Refinement and Validation
  16. ...and 19 more sections

Figures (4)

  • Figure 1: Distribution of optimal configuration choices across tasks and hardware platforms. The choice of efficiency techniques varies significantly with task type and hardware constraints, validating the need for adaptive optimization.
  • Figure 2: Pareto fronts showing accuracy-latency trade-offs for different models. AE-LLM identifies configurations across the full spectrum of trade-offs, enabling practitioners to select based on their specific requirements.
  • Figure 3: Scatter plot showing the relationship between efficiency improvements and accuracy changes. Different efficiency techniques exhibit distinct trade-off patterns.
  • Figure 4: Sensitivity of performance metrics to key hyperparameters. The shaded regions indicate the range of performance across different tasks.

Theorems & Definitions (4)

  • Definition 1: Efficiency Configuration
  • Definition 2: Performance Objectives
  • Definition 3: Hardware Constraints
  • Definition 4: Adaptive Efficiency Optimization Problem