The Compression Paradox in LLM Inference: Provider-Dependent Energy Effects of Prompt Compression
arXiv:2603.23528v1 Announce Type: new Abstract: The rapid proliferation of Large Language Models has created an environmental paradox: the very technology that could help solve climate challenges is itself becoming a significant contributor to global carbon emissions. We test whether prompt compression improves inference energy efficiency in 28,421 successful API trials (28,428 planned) across three providers (OpenAI GPT-4o-mini, Anthropic Claude-3.5-Sonnet, and DeepSeek-Chat), five benchmarks (HumanEval, MBPP, GSM8K, MATH, MMLU), and four compression ratios (r in {1.0, 0.7, 0.5, 0.3}). Energy is estimated with a token-based proxy calibrated against local direct measurements, and quality is tracked with benchmark pass rates. Compression produced substantial quality loss (overall pass rate 26.0% at baseline vs. 1.5% at r=0.7) and strongly provider-dependent energy behavior. DeepSeek exhibited output expansion under compression (21 to 798 tokens at r=0.3), corresponding to energy increa
arXiv:2603.23528v1 Announce Type: new Abstract: The rapid proliferation of Large Language Models has created an environmental paradox: the very technology that could help solve climate challenges is itself becoming a significant contributor to global carbon emissions. We test whether prompt compression improves inference energy efficiency in 28,421 successful API trials (28,428 planned) across three providers (OpenAI GPT-4o-mini, Anthropic Claude-3.5-Sonnet, and DeepSeek-Chat), five benchmarks (HumanEval, MBPP, GSM8K, MATH, MMLU), and four compression ratios (r in {1.0, 0.7, 0.5, 0.3}). Energy is estimated with a token-based proxy calibrated against local direct measurements, and quality is tracked with benchmark pass rates. Compression produced substantial quality loss (overall pass rate 26.0% at baseline vs. 1.5% at r=0.7) and strongly provider-dependent energy behavior. DeepSeek exhibited output expansion under compression (21 to 798 tokens at r=0.3), corresponding to energy increases up to +2,140%, while GPT-4o-mini showed mixed effects including a reduction at r=0.5. These results indicate that input-token reduction alone is not a reliable energy optimization strategy in production inference. For the evaluated settings, model selection and output-length control provided more consistent energy-quality tradeoffs than prompt compression.
Executive Summary
This study investigates the compression paradox in Large Language Model (LLM) inference, where energy efficiency is compromised despite input-token reduction. The authors conducted extensive API trials across three LLM providers, five benchmarks, and four compression ratios. The results indicate that prompt compression leads to significant quality loss and provider-dependent energy behavior, with some models exhibiting energy increases of up to 2,140%. The study concludes that input-token reduction alone is not a reliable energy optimization strategy, suggesting that model selection and output-length control offer more consistent energy-quality tradeoffs. These findings have significant implications for the deployment and development of LLMs, particularly in light of their rapidly growing environmental footprint.
Key Points
- ▸ Prompt compression can lead to significant quality loss in LLM inference.
- ▸ LLM providers exhibit provider-dependent energy behavior under compression.
- ▸ Model selection and output-length control offer more consistent energy-quality tradeoffs than prompt compression.
Merits
Insightful Analysis
The study provides a comprehensive analysis of the compression paradox in LLM inference, shedding light on the complex interplay between energy efficiency, model performance, and provider-specific behavior.
Methodological Rigor
The authors employed a rigorous methodology, conducting extensive API trials across multiple providers, benchmarks, and compression ratios, ensuring the validity and generalizability of their findings.
Practical Implications
The study's findings have significant practical implications for the deployment and development of LLMs, highlighting the need for more efficient and sustainable computing solutions.
Demerits
Limited Generalizability
The study's findings may not be generalizable to all LLM providers and models, highlighting the need for further research to validate the results and explore potential exceptions.
Insufficient Context
The study's focus on energy efficiency and model performance may overlook other important aspects of LLM inference, such as interpretability, explainability, and fairness.
Expert Commentary
The study's findings are significant and timely, highlighting the need for more efficient and sustainable computing solutions in the LLM inference space. While the study's methodology is rigorous and well-executed, the results may not be generalizable to all LLM providers and models, emphasizing the need for further research. The study's focus on energy efficiency and model performance is complemented by the related issues of environmental impact and model interpretability, emphasizing the importance of considering the broader implications of LLM inference. The study's implications are far-reaching, with practical implications for LLM developers and deployers and policy implications for policymakers. Overall, the study is a valuable contribution to the field, highlighting the need for more sustainable and efficient computing solutions in LLM inference.
Recommendations
- ✓ Future studies should explore the generalizability of the study's findings across multiple LLM providers and models, as well as the impact of compression on other aspects of LLM inference, such as interpretability and explainability.
- ✓ Developers and deployers of LLMs should prioritize energy-efficient computing solutions and consider model selection and output-length control as alternative strategies to prompt compression.
Sources
Original: arXiv - cs.CL
