Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations
A large-scale empirical study examines how different vLLM inference engine configurations—specifically attention kernel type, prefix caching, and chunked prefill—affect energy consumption, latency, and accuracy across multiple open-weight LLMs and tasks. The findings show that attention kernel type and prefix caching have significant impacts on energy and performance, while chunked prefill has limited effect under default settings. The optimal configuration is highly dependent on the specific model and workload, with no single configuration performing best in all scenarios.
Why it matters: This research provides practical guidance for optimizing LLM inference deployments, demonstrating that while configuration tuning can yield local improvements, the choice of model has a greater overall impact on trade-offs between energy, performance, and accuracy.
Full story at: arXiv Software Engineering ↗