Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters
arXiv:2602.17697v1 Announce Type: new Abstract: Large Language Models (LLMs) are being increasingly used across a wide range of tasks. However, their substantial computational demands raise concerns about the energy efficiency and sustainability of both training and inference. Inference, in particular, dominates total compute usage, making its optimization crucial. Recent research has explored optimization techniques and analyzed how configuration choices influence energy consumption. Yet, the vast configuration space of inference servers makes exhaustive empirical evaluation infeasible due to combinatorial explosion. In this paper, we introduce a new perspective on this problem by treating LLMs as configurable systems and applying variability management techniques to systematically analyze inference-time configuration choices. We evaluate our approach on the Hugging Face Transformers library by representing generation hyperparameters and their constraints using a feature-based variab
arXiv:2602.17697v1 Announce Type: new Abstract: Large Language Models (LLMs) are being increasingly used across a wide range of tasks. However, their substantial computational demands raise concerns about the energy efficiency and sustainability of both training and inference. Inference, in particular, dominates total compute usage, making its optimization crucial. Recent research has explored optimization techniques and analyzed how configuration choices influence energy consumption. Yet, the vast configuration space of inference servers makes exhaustive empirical evaluation infeasible due to combinatorial explosion. In this paper, we introduce a new perspective on this problem by treating LLMs as configurable systems and applying variability management techniques to systematically analyze inference-time configuration choices. We evaluate our approach on the Hugging Face Transformers library by representing generation hyperparameters and their constraints using a feature-based variability model, sampling representative configurations, measuring their energy consumption, latency, accuracy, and learning predictive models from the collected data. Our results show that variability modeling effectively manages the complexity of LLM inference configurations. It enables systematic analysis of hyperparameters effects and interactions, reveals trade-offs, and supports accurate prediction of inference behavior from a limited number of measurements. Overall, this work opens a new research direction that bridges software engineering and machine learning by leveraging variability modeling for the efficient and sustainable configuration of LLMs.
Executive Summary
This article proposes a novel approach to optimize inference hyperparameters of Large Language Models (LLMs) using variability modeling techniques. By treating LLMs as configurable systems, the authors apply variability management techniques to systematically analyze inference-time configuration choices. The approach is demonstrated on the Hugging Face Transformers library, where generation hyperparameters and constraints are represented using a feature-based variability model. The results show that variability modeling effectively manages the complexity of LLM inference configurations, enabling systematic analysis of hyperparameters effects and interactions. This work bridges software engineering and machine learning, opening a new research direction for efficient and sustainable configuration of LLMs. The approach has the potential to reduce energy consumption and latency, making LLMs more practical for real-world applications.
Key Points
- ▸ Variability modeling is applied to systematically analyze inference-time configuration choices of LLMs.
- ▸ A feature-based variability model is used to represent generation hyperparameters and constraints.
- ▸ The approach enables systematic analysis of hyperparameters effects and interactions.
Merits
Strength in Methodology
The use of variability modeling provides a structured approach to analyzing the vast configuration space of LLM inference servers, making it a strength of the paper's methodology.
Applicability
The approach is demonstrated on a widely used library (Hugging Face Transformers), making it applicable to a broad range of tasks and users.
Potential Impact
The reduction in energy consumption and latency has the potential to make LLMs more practical for real-world applications, making it a significant contribution to the field.
Demerits
Limited Scope
The paper focuses on a specific aspect of LLMs (inference hyperparameters) and may not generalize to other aspects, such as model training or other machine learning tasks.
Computational Requirements
The approach requires significant computational resources to collect and analyze the data, which may be a limitation for users with limited resources.
Expert Commentary
This paper presents a novel approach to optimizing inference hyperparameters of LLMs using variability modeling techniques. The use of variability modeling provides a structured approach to analyzing the vast configuration space of LLM inference servers, making it a strength of the paper's methodology. The approach is demonstrated on a widely used library (Hugging Face Transformers), making it applicable to a broad range of tasks and users. However, the paper focuses on a specific aspect of LLMs (inference hyperparameters) and may not generalize to other aspects, such as model training or other machine learning tasks. Additionally, the approach requires significant computational resources to collect and analyze the data, which may be a limitation for users with limited resources. Overall, the paper presents a significant contribution to the field, highlighting the need for more efficient and sustainable configuration of LLMs.
Recommendations
- ✓ Further research is needed to generalize the approach to other aspects of LLMs and other machine learning tasks.
- ✓ The approach should be tested on a wider range of tasks and datasets to evaluate its robustness and scalability.
