Can We Optimize the Performance-Carbon Emission Break-Even Point?: The Quest for Greener LLMs
The carbon footprint of any deployed Large Language Model (LLM) accumulates during inference, where repeated use of the model substantially exceeds the one-time cost of fine-tuning. Yet most efficiency interventions target either pre-training scale or post-hoc compression. We ask whether folding a calibrated, differentiable energy surrogate into the fine-tuning objective can produce inference behavior that gains task accuracy at zero or near-zero carbon cost, a break-even configuration. We propose a joint loss mechanism with a per-model carbon-emission parameter, a linear surrogate over parameter norm, FLOP proxy, and a memory proxy, fit from on-hardware energy profiling. We fine-tune thr...
| Authors | Sourav Das, Tanmay Joshi, Kripabandhu Ghosh |
|---|---|
| Primary category | cs.CL |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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