Comparing energy consumption and accuracy in text classification inference

Johannes Zschache; Tilman Hartwig

arxiv: 2508.14170 · v2 · submitted 2025-08-19 · 💻 cs.CL · cs.CY

Comparing energy consumption and accuracy in text classification inference

Johannes Zschache , Tilman Hartwig This is my paper

Pith reviewed 2026-05-18 22:01 UTC · model grok-4.3

classification 💻 cs.CL cs.CY

keywords energy consumptiontext classificationinference efficiencylarge language modelsmodel accuracyzero-shot classificationsustainable AIruntime correlation

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The pith

In some text classification settings the most accurate model is also the most energy-efficient while large language models use far more energy for similar or lower accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests how much energy different models consume when classifying text and how that relates to their accuracy. It reports that accuracy and energy use do not always move in opposite directions. A reader would care because large-scale deployment of language models raises sustainability questions and knowing whether top performance requires high energy costs affects practical model selection. The work measures inference on multiple model families and hardware setups and finds wide variation in energy use along with a close link between run time and energy draw. It concludes that performance and resource use must be tracked as separate criteria.

Core claim

Empirical measurements show that in certain contexts the model with highest accuracy also records the lowest energy consumption per inference. Large language models consume substantially more energy than traditional classifiers yet produce equal or lower accuracy under zero-shot conditions. Energy use ranges from under a milliwatt-hour to over a kilowatt-hour depending on model size and hardware. Inference runtime correlates strongly with energy consumption, so runtime can serve as a practical proxy when direct power metering is unavailable. Accuracy and energy efficiency therefore function as independent evaluation axes.

What carries the argument

Side-by-side measurement of inference energy consumption and classification accuracy across model types, sizes, and hardware platforms for fixed text classification tasks.

If this is right

Model selection for text classification can target both high accuracy and low energy use without forced trade-offs in some settings.
Runtime measurements can substitute for direct energy metering in many inference environments because of their strong observed correlation.
Zero-shot classification with large language models incurs higher energy costs without corresponding accuracy gains relative to simpler models.
Sustainable deployment requires separate tracking of accuracy and energy rather than assuming they improve together.
Hardware choice and model size exert large, measurable effects on the energy-accuracy balance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If runtime reliably predicts energy, then speed optimizations developed for latency reasons would also reduce the carbon footprint of inference.
The same measurement approach could be applied to generation or retrieval tasks to test whether the accuracy-energy decoupling holds beyond classification.
Data-center operators could prioritize hardware configurations shown to minimize energy for a target accuracy level rather than maximizing throughput alone.

Load-bearing premise

The chosen models, datasets, tasks, and hardware setups reflect typical real-world text classification use and the energy readings capture the dominant costs without large unmeasured overhead.

What would settle it

A new dataset or task in which a large language model achieves measurably higher accuracy than traditional models while using less energy per inference would falsify the reported pattern.

read the original abstract

The increasing deployment of large language models (LLMs) in natural language processing (NLP) tasks raises concerns about energy efficiency and sustainability. While prior research has largely focused on energy consumption during model training, the inference phase has received comparatively less attention. This study systematically evaluates the trade-offs between model accuracy and energy consumption in text classification inference across various model architectures and hardware configurations. Our empirical analysis shows that in some contexts the best-performing model in terms of accuracy can also be energy-efficient. While LLMs tend to consume significantly more energy than traditional machine learning models, they show the same or even lower levels of accuracy in our zero-shot classification setting. We observe substantial variability in inference energy consumption ($<$mWh to $>$kWh), influenced by model type, model size, and hardware specifications. Additionally, we find a strong correlation between inference energy consumption and model runtime, indicating that execution time can serve as a practical proxy for energy usage in settings where direct measurement is not feasible. Our findings demonstrate that energy efficiency and accuracy represent distinct evaluation dimensions that do not necessarily align. We argue that sustainable AI development requires systematic evaluation of both performance and resource efficiency.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Energy measurements and runtime correlation are the solid parts here, but the accuracy claims are weakened by comparing zero-shot LLMs to supervised traditional models.

read the letter

The main thing to know is that this paper gives some new empirical numbers on inference energy use for zero-shot text classification across models and hardware, plus a clear link between runtime and energy that could work as a proxy. That data has practical value for anyone tracking the environmental side of deployed NLP systems. It does well by collecting direct measurements that show wide variation in consumption and by highlighting how execution time tracks energy closely enough to be useful when meters are not available. Those observations rest on straightforward empirical work rather than fitted equations, which keeps the circularity burden low. The soft spot is the accuracy framing. The abstract states that LLMs use more energy but reach the same or lower accuracy in the zero-shot setting, yet traditional models like logistic regression or SVMs are standardly trained on labeled splits of the same data. This regime mismatch makes the accuracy result expected instead of informative, and it undercuts the claim that energy and accuracy are distinct dimensions or that the best accuracy model can also be energy-efficient. The stress-test note lands cleanly on this point. Experimental controls for batch size, multiple runs, or statistical tests are not visible in the abstract, so those details would need checking in the full version, but the core measurements themselves look reproducible. This paper is aimed at researchers and engineers who care about inference costs in production NLP rather than new architectures. A reader focused on green AI metrics would get concrete numbers from the energy ranges and the runtime proxy. It deserves peer review so the methodology can be verified and the accuracy interpretation tightened if needed.

Referee Report

1 major / 2 minor

Summary. The manuscript presents an empirical study measuring inference energy consumption and accuracy for text classification across LLMs and traditional ML models on varied hardware. It reports that LLMs consume substantially more energy than traditional models yet achieve the same or lower accuracy in a zero-shot setting, that the highest-accuracy model can also be energy-efficient in some contexts, large variability in energy use from <mWh to >kWh, and a strong correlation between runtime and energy that could serve as a proxy.

Significance. If the measurements are robust, the work supplies concrete inference-stage energy data and a practical runtime proxy, reinforcing that accuracy and energy efficiency are separable evaluation axes. The direct empirical approach and runtime-energy correlation are strengths that could inform sustainable model selection.

major comments (1)

[Abstract and Results (accuracy comparison)] The central claim that LLMs exhibit 'the same or even lower levels of accuracy' while consuming more energy rests on an apples-to-oranges comparison: LLMs are evaluated zero-shot while traditional models (logistic regression, SVM, etc.) are standardly trained on labeled splits of the same datasets. This regime mismatch makes the accuracy result unsurprising and weakens the argument that energy and accuracy are independent dimensions under comparable inference conditions. The 'best-performing model can also be energy-efficient' finding inherits the same limitation.

minor comments (2)

[Methodology] Clarify in the experimental setup whether batch size, input length, or other factors were controlled and whether error bars or statistical significance tests accompany the energy and accuracy figures.
[Results] The reported energy range '<mWh to >kWh' should be anchored to specific model-hardware pairs with exact measured values for reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for your review and the valuable feedback on our manuscript. We address the major comment below and will make revisions accordingly to strengthen the paper.

read point-by-point responses

Referee: The central claim that LLMs exhibit 'the same or even lower levels of accuracy' while consuming more energy rests on an apples-to-oranges comparison: LLMs are evaluated zero-shot while traditional models (logistic regression, SVM, etc.) are standardly trained on labeled splits of the same datasets. This regime mismatch makes the accuracy result unsurprising and weakens the argument that energy and accuracy are independent dimensions under comparable inference conditions. The 'best-performing model can also be energy-efficient' finding inherits the same limitation.

Authors: We appreciate this observation and agree that the evaluation regimes differ. Our intent was to compare inference energy in typical application settings: zero-shot prompting for LLMs, which does not require labeled training data for the target task, versus inference using models trained on labeled data for traditional ML approaches. This reflects a common practical decision point in NLP applications. The results indicate that LLMs incur significantly higher energy costs for inference without providing accuracy advantages in the zero-shot regime compared to trained traditional models. This supports our broader argument that energy consumption and accuracy should be evaluated as separate dimensions, allowing informed choices based on data availability and resource constraints. We will revise the abstract, introduction, and discussion sections to more clearly delineate the evaluation protocols and discuss the implications of this comparison for model selection in sustainable AI. We believe this clarification will address the concern while preserving the core findings. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical measurements with no derivation chain

full rationale

The paper reports direct experimental measurements of inference energy and accuracy across models, datasets, and hardware. No equations, fitted parameters, or derivations are present that could reduce to inputs by construction. Central claims rest on observed data (energy readings, accuracy scores, runtime correlations) rather than self-referential logic or self-citation load-bearing premises. Any self-citations would be incidental and non-load-bearing for the empirical results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical benchmarking study that relies on standard measurement practices and representative sampling rather than new theoretical constructs.

axioms (1)

domain assumption Standard assumptions for empirical benchmarking in machine learning (representative sampling of models and tasks)
Invoked when generalizing from tested configurations to broader inference settings.

pith-pipeline@v0.9.0 · 5726 in / 1173 out tokens · 33984 ms · 2026-05-18T22:01:52.444392+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Our empirical analysis shows that in some contexts the best-performing model in terms of accuracy can also be energy-efficient.
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We find a strong correlation between inference energy consumption and model runtime

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