Tu(r)ning AI Green: Exploring Energy Efficiency Cascading With Orthogonal Optimizations
Bibliographic record
Abstract
AI’s exponential growth intensifies computational demands and energy challenges. While practitioners employ various optimization techniques, that we refer as “knobs” in this paper, to tune model efficiency, these are typically afterthoughts and applied in isolation without understanding their combined effects on energy efficiency. The goal of this exploratory empirical study is to emphasize on treating energy efficiency as the firstclass design consideration by demonstrating how strategic knob selection across five AI pipeline stages (data, model, training, system, inference) creates cascading efficiency gains. We evaluate 30 experimental variants on ModernBERT, an encoderonly architecture, examining individual techniques and their orthogonal combinations. Results shows that model pruning provides the highest single-knob energy savings (up to 84.6%), while orthogonal combinations reduce energy consumption by up to 94.6% while preserving 95.95% of baseline F1 score. This work provides actionable frameworks for informed green AI that balance efficiency, performance, and environmental responsibility in AI systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".