How vLLM Settings Reshape Energy, Speed, and Accuracy in Language Model Serving

Researchers have conducted an extensive evaluation of how different vLLM inference engine settings affect the energy consumption, latency, and output quality of large language models in production deployments.

The Study Design#

The team systematically tested three key configuration options—attention kernel type, prefix caching, and chunked prefill—across 5 open-source LLMs and 5 distinct inference workloads. Their controlled experiments encompassed 9,000 runs and generated over 93,600 individual measurements, providing a comprehensive picture of how these settings influence real-world model serving.

What the Results Show#

The findings demonstrate that attention type and prefix caching drive most of the variation in energy use and performance metrics. Chunked prefill, by contrast, produced minimal effects under typical vLLM serving conditions with the evaluated tasks. Importantly, the impact of each configuration option depends heavily on both the specific model and the workload being run—meaning there is no universally optimal setup that works best across all scenarios.

The research also uncovered an unexpected finding: tweaking inference settings can actually alter model accuracy, not just speed and power consumption. While model selection remains the dominant factor shaping overall trade-offs between efficiency and quality, careful configuration tuning can deliver incremental improvements along the efficiency frontier.

Why It Matters#

As language models become standard tools in software development and maintenance, understanding how to configure their serving infrastructure becomes crucial. The study reveals that teams cannot simply apply a single vLLM setup across different use cases—instead, they must tune settings based on their particular model and task demands to achieve their desired balance between energy efficiency, response time, and output correctness.

View the full research paper on arXiv