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× 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.
{FP16, FP8, INT8, INT4} via {GPTQ, AWQ, SmoothQuant}
KV Cache
{Full, MQA-style, GQA-style}
Table 1: AE-LLM Configuration Space Design.
Model
Method
Accuracy (%)
Latency (ms)
Memory (GB)
Energy (J)
Efficiency Score
Small Models (0.5B–2B)
LLaMA-2-1B
Default
43.2
12.5
2.1
0.08
1.00
Best Single-Stage
42.8
9.2
1.8
0.06
1.31
Manual Selection
42.5
8.5
1.6
0.05
1.45
EfficientLLM Rec.
42.1
8.8
1.7
0.05
1.38
AdaptiveEfficientLLM
43.0
7.8
1.4
0.04
1.72
Table 2: Main results comparing AdaptiveEfficientLLM against baselines. We report accuracy (higher is better), latency in milliseconds (lower is better), memory in GB (lower is better), and energy in Joules (lower is better). Best results are in bold , second best are underlined . Efficiency Score is the geometric mean of normalized efficiency metrics, higher is better.
Configuration
Efficiency Score
Rel. Improvement
Search Algorithm Components
Full AdaptiveEfficientLLM
1.95
+95%
- Predictive Models (random search)
1.62
+62%
- Constraint-Aware Pruning
1.92
+92%
- Hierarchical Crossover
1.85
+85%
- Refinement Iterations
1.82
+82%
Table 3: Ablation studies on LLaMA-2-7B. We report the efficiency score and relative improvement compared to the default configuration.
Model
Method
Accuracy
Lat. (ms)
Mem. (GB)
En. (J)
VQAv2 Benchmark
LLaVA-1.5-7B
Default
78.5
85.2
18.5
1.25
EfficientLLM Rec.
77.8
68.5
14.2
0.95
AdaptiveEfficientLLM
78.2
52.8
10.8
0.72
InternVL-Chat
Default
81.2
92.5
22.5
1.42
EfficientLLM Rec.
80.5
75.8
17.5
1.15
Table 4: Cross-modal generalization results on vision-language models. We report accuracy (VQA score for VQAv2, CIDEr for COCO Caption, accuracy for TextVQA) and efficiency metrics.
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.
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
XGBoost Regressor
Value
NSGA-II Search
Value
Estimators / Max Depth
500 / 8
Population Size / Gen
100 / 50
Learning Rate
0.05
Crossover / Mutation
0.9 / Variable
Subsample / Colsample
0.8 / 0.8
Tournament Size
3
Appendix
Table 5: Hyperparameter settings for surrogate models and NSGA-II search.
Model
Method
MMLU
HellaS.
ARC-E
GSM8K
HumanE.
Alpaca
LongB.
Needle
MT-B
Vicuna
Avg.
LLaMA-2-7B
Default
46.8
78.2
72.5
14.5
12.8
85.2
32.5
88.5
6.2
78.5
51.6
Best Single-Stage
46.2
77.8
72.0
14.0
12.2
84.5
31.8
87.8
6.0
77.8
51.0
Manual Selection
45.8
77.2
71.5
13.5
11.8
84.0
31.2
87.2
5.8
77.2
50.5
EfficientLLM Rec.
46.0
77.5
71.8
13.8
12.0
84.2
31.5
87.5
5.9
77.5
50.8
AdaptiveEfficientLLM
46.5
78.0
72.2
14.2
12.5
85.0
32.2
88.2
6.1
78.2
51.3
Mistral-7B
Default
62.5
82.8
78.2
37.5
26.2
92.5
38.5
92.8
7.5
85.2
60.4
Appendix
Table 6: Detailed results across all tasks for selected models. We report task-specific accuracy metrics.