The use of generative AI-based coding assistants like ChatGPT and Github Copilot is a reality in contemporary software development. Many of these tools are provided as remote APIs. Using third-party APIs raises data privacy and security concerns for client companies, which motivates the use of locallydeployed language models. In this study, we explore the tradeoff between model accuracy and energy consumption, aiming to provide valuable insights to help developers make informed decisions when selecting a language model. We investigate the performance of 18 families of LLMs in typical software development tasks on two real-world infrastructures, a commodity GPU and a powerful AI-specific GPU. Given that deploying LLMs locally requires powerful infrastructure which might not be affordable for everyone, we consider both full-precision and quantized models. Our findings reveal that employing a big LLM with a higher energy budget does not always translate to significantly improved accuracy. Additionally, quantized versions of large models generally offer better efficiency and accuracy compared to full-precision versions of medium-sized ones. Apart from that, not a single model is suitable for all types of software development tasks.

Deep Learning (DL) frameworks such as PyTorch and TensorFlow include runtime infrastructures responsible for executing trained models on target hardware, managing memory, data transfers, and multi-accelerator execution, if applicable. Additionally, it is a common practice to deploy pre-trained models on environments distinct from their native development settings. This led to the introduction of interchange formats such as ONNX, which includes its runtime infrastructure, and ONNX Runtime, which work as standard formats that can be used across diverse DL frameworks and languages. Even though these runtime infrastructures have a great impact on inference performance, no previous paper has investigated their energy efficiency. In this study, we monitor the energy consumption and inference time in the runtime infrastructures of three well-known DL frameworks as well as ONNX, using three various DL models. To have nuance in our investigation, we also examine the impact of using different execution providers. We find out that the performance and energy efficiency of DL are difficult to predict. One framework, MXNet, outperforms both PyTorch and TensorFlow for the computer vision models using batch size 1, due to efficient GPU usage and thus low CPU usage. However, batch size 64 makes PyTorch and MXNet practically indistinguishable, while TensorFlow is outperformed consistently. For BERT, PyTorch exhibits the best performance. Converting the models to ONNX yields significant performance improvements in the majority of cases. Finally, in our preliminary investigation of execution providers, we observe that TensorRT always outperforms CUDA.