mlsanityData to 14 Oct 2026

Transparent Screening for LLM Inference and Training Impacts

arXiv 2604.19757 ↗First posted 23 Mar 2026cs.LG cs.AI cs.CLcited by 0

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Abstract (arXiv)

This paper presents a transparent screening framework for estimating inference and training impacts of current large language models under limited observability. The framework converts natural-language application descriptions into bounded environmental estimates and supports a comparative online observatory of current market models. Rather than claiming direct measurement for opaque proprietary services, it provides an auditable, source-linked proxy methodology designed to improve comparability, transparency, and reproducibility.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 23 Mar 2026.

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6 of 25 references.

References · 6

  1. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  2. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
  3. 2024A Survey on Efficient Inference for Large Language Modelscited by 205
  4. 2025The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimizationcited by 33
  5. 2025Measuring the environmental impact of delivering AI at Google Scalecited by 32
  6. 2025Holistically Evaluating the Environmental Impact of Creating Language Modelscited by 26

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