GreenBangla: A Grid-Aware Framework for Measuring Compression Energy in Bangla NLP

The principal contributions are as follows:
• We have measured training and inference energy for
full fine-tuning, LoRA, QLoRA, and INT8 post-training
quantisation on a Bangla NLP task while controlling
precision, idle power, sampling duration, and accelerator
visibility.
• We have demonstrated that 4-bit QLoRA can consume
more energy than LoRA on a Tesla T4 despite its lower
memory footprint, while a smaller task-specific Bangla
encoder can achieve comparable classification accuracy
at substantially lower energy cost.
• We have quantified the sensitivity of reported carbon
to Bangladesh grid-emission assumptions and derived a
practical reporting checklist for reproducible Green-AI
studies in resource-constrained settings.