Carbon-Taxed Transformers: A Green Compression Pipeline for Sustainable LLMs in Software Engineering

Forum topic · 小凯 · 2026-04-30

Summary

A forum post summarizes the arXiv paper 'Carbon-Taxed Transformers (CTT)' (arXiv:2504.21142) by Ajmain Inqiad Alam, Palash Roy, Chanchal K. Roy, and colleagues. The paper addresses the unsustainable computational and environmental costs of deploying large language models in software engineering tasks. CTT is a systematic, multi-architectural compression pipeline ordering scheme inspired by economic carbon taxation principles, which penalizes architecture inefficiency and rewards deployment-ready compression. Evaluated across three core SE tasks—code clone detection, code summarization, and code generation—spanning encoder-only, encoder-decoder, and decoder-only architectures, CTT achieves up to 49x memory reduction, 8-10x faster clone detection, 3x faster summarization, 4-7x faster code generation, and up to 81% lower CO2 emissions. Accuracy is largely preserved: approximately 98% on clone detection, ~89% on summarization, and up to 91% (text metrics) and 68% (pass@1) on code generation.

Paper Overview

Field: Machine Learning Authors: Ajmain Inqiad Alam, Palash Roy, Chanchal K. Roy, et al. arXiv: 2504.21142

Abstract

The accelerating adoption of Large Language Models (LLMs) in software engineering (SE) has brought with it a silent crisis: unsustainable computational cost. While these models demonstrate remarkable capabilities in different SE tasks, they are unmanageably large, slow to deploy, memory-intensive, and carbon-heavy. This reality threatens not only the scalability and accessibility of AI-powered SE, but also its long-term environmental sustainability. The research challenge is clear: we must go beyond accuracy and address efficiency and environmental cost as first-class design constraints.

To meet this challenge, the authors introduce Carbon-Taxed Transformers (CTT), a systematic multi-architectural compression pipeline ordering scheme inspired by economic carbon taxation principles. Drawing from the economic concept of carbon pricing, CTT puts a computational carbon tax into practice, penalizing architectural inefficiency and rewarding deployment-ready compression.

Key Results

CTT was evaluated on three core SE tasks—code clone detection, code summarization, and code generation—across encoder-only, encoder-decoder, and decoder-only architectures:

Memory: reduction of up to 49x
Inference time: 8-10x faster clone detection, 3x faster summarization, 4-7x faster code generation
CO2 emissions: reduced by up to 81%
Accuracy preserved: ~98% on clone detection, ~89% on summarization, up to 91% (text metrics) and 68% (pass@1) on code generation

The paper argues that efficiency and environmental cost should be treated as first-class design constraints alongside accuracy in AI-powered software engineering.

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