📝 SummarXiv

Abstract

The rapid expansion of large language models (LLMs) has heightened concerns about their computational and environmental costs. This study investigates the trade-offs between translation quality and efficiency by comparing full-scale, distilled, and quantized models using machine translation as a case study. We evaluated performance on the Flores+ benchmark and through human judgments of conversational translations in French, Hindi, and Kannada. Our analysis of carbon emissions per evaluation run revealed that the full 3.3B fp32 model, while achieving the highest BLEU scores, incurred the largest environmental footprint (about 0.007-0.008 kg CO2 per run). The distilled models achieved an inference of up to 4.5x faster than the full 3.3B model, with only minimal reductions in BLEU scores. Human evaluations also showed that even aggressive quantization (INT4) preserved high levels of accuracy and fluency, with differences between models generally minor. These findings demonstrate that model compression strategies can substantially reduce computational demands and environmental impact while maintaining competitive translation quality, though trade-offs are more pronounced in low-resource settings. We argue for evaluation frameworks that integrate efficiency and sustainability alongside objective metrics as central dimensions of progress in NLP.