A new research paper investigates the trade-offs between energy consumption, performance, and accuracy when configuring inference engines like vLLM for large language models (LLMs). The study analyzed combinations of attention kernel type, prefix caching, and chunked prefill across five open-weight LLMs and five inference tasks. Results indicate that attention type and prefix caching significantly impact energy and performance, with effects varying by model and workload, while chunked prefill showed limited impact under default configurations. The research also found that inference engine choices can unexpectedly influence model accuracy. AI
IMPACT Configuration tuning of inference engines like vLLM can optimize LLM deployment for energy efficiency and performance without sacrificing accuracy.
RANK_REASON Research paper analyzing configuration trade-offs for LLM inference engines.
- attention kernel type
- chunked prefill
- Hugging Face
- Large Language Models
- open-weight LLMs
- Prefix Caching
- vLLM
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