  Large Language Models (LLMs) can automate or substitute different types of
tasks in the software engineering process. This study evaluates the resource
utilization and accuracy of LLM in interpreting and executing natural language
queries against traditional SQL within relational database management systems.
We empirically examine the resource utilization and accuracy of nine LLMs
varying from 7 to 34 Billion parameters, including Llama2 7B, Llama2 13B,
Mistral, Mixtral, Optimus-7B, SUS-chat-34B, platypus-yi-34b,
NeuralHermes-2.5-Mistral-7B and Starling-LM-7B-alpha, using a small transaction
dataset. Our findings indicate that using LLMs for database queries incurs
significant energy overhead (even small and quantized models), making it an
environmentally unfriendly approach. Therefore, we advise against replacing
relational databases with LLMs due to their substantial resource utilization.
