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SEALing the Gap: A Reference Framework for LLM Inference Carbon Estimation via Multi-Benchmark Driven Embodiment

arXiv – CS AI|Priyavanshi Pathania, Rohit Mehra, Vibhu Saujanya Sharma, Vikrant Kaulgud, Tiffani Nevels, Sanjay Podder, Adam P. Burden|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers have developed SEAL, a reference framework for measuring carbon emissions from Large Language Model inference at the prompt level. The framework addresses the growing sustainability concerns as LLM inference emissions are rapidly surpassing training emissions due to massive usage volumes.

Key Takeaways

→LLM inference emissions are quickly surpassing training emissions due to the high volume of prompts processed.
→SEAL introduces a multi-benchmark-driven approach for per-prompt carbon estimation during LLM inference.
→The framework provides guiding principles for future sustainability tools in the LLM ecosystem.
→Initial validation of SEAL shows promising results for standardized sustainability assessment.
→Accurate carbon measurement at the prompt level enables informed sustainability-focused decision-making.

#ai #sustainability #carbon-footprint #llm #inference #environmental-impact #research #framework

Read Original →via arXiv – CS AI

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