Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations

2026-07-13 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

A large-scale controlled study evaluated three vLLM configuration options—attention kernel type, prefix caching, and chunked prefill—on energy consumption, latency, and accuracy. Researchers conducted 9,000 runs and 93,600 measures across 5 open-weight LLMs (3B to 32B parameters) and 5 diverse inference tasks. Experiments utilized a cluster with 4 Nvidia A100-SXM4-40GB GPUs, vLLM 0.10.2, FlashInfer 0.4.1, and CUDA 12.2.128. Findings show attention type and prefix caching significantly impact energy and performance, with effects varying by model and workload. No universal optimal configuration exists; model choice dictates global trade-offs. Notably, inference configurations can unexpectedly influence model accuracy.

Key takeaway

For AI Engineers deploying vLLM-served LLMs, you must actively evaluate configuration options for your specific models and tasks. Do not assume default settings are optimal or that published benchmark accuracy will transfer directly. Your choice of attention kernel and prefix caching significantly impacts energy, latency, and even accuracy. Use Pareto frontiers to balance these objectives, prioritizing FlashInfer for energy or FlashAttention-3 for latency based on your primary goal.

Key insights

vLLM configuration choices significantly impact LLM inference energy, performance, and surprisingly, accuracy, with no universal optimal.

Principles

• Optimal vLLM settings are model- and task-specific.
• Attention kernel and prefix caching are key levers.
• Inference options can affect model accuracy.

Method

A full-factorial controlled experiment evaluated three vLLM options across five LLMs and five tasks, with 30 repetitions each, analyzing energy, latency, and accuracy.

In practice

• Prioritize FlashInfer for energy, FlashAttention-3 for latency.
• Evaluate attention kernel and prefix caching jointly.
• Use Pareto frontiers for multi-objective optimization.

Topics

• vLLM
• LLM Inference
• Energy Efficiency
• Performance Optimization
• Attention Kernels
• Prefix Caching
• Model Accuracy

Best for: MLOps Engineer, NLP Engineer, AI Scientist, AI Engineer, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.