Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations
Large Language Models are reshaping how software is developed and maintained. They are typically deployed in production using inference engines such as vLLM, which can efficiently serve pre-trained, highly configurable models. While prior work has focused on model architectures and hardware acceleration, the impact of inference engine configuration on energy consumption, performance, and output quality remains poorly understood. In this paper, we present a large-scale controlled study of three selected vLLM configuration options: attention kernel type, prefix caching, and chunked prefill. We evaluate all combinations of these configurations across 5 open-weight LLMs and 5 diverse inferenc...
| Authors | Nada Zine, Tristan Coignion, Vincenzo Stoico et al. |
|---|---|
| Primary category | cs.SE |
| Published | 10 July 2026 |
| Last updated at source | 17 July 2026 |
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