Paper Citation Record · LEDGER

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 11 inbound Pith citation observations for arXiv:2507.11417.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.11417 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:14:19.390806Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:19:15.470025Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy12
unresolved17
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External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 3edf7c7e-3760-40ee-a2d8-2296cdd50903 · outbound

This paper cites In: USENIX Symposium on Operating Systems Design and Imple- mentation (OSDI) (2024).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: USENIX Symposium on Operating Systems Design and Imple- mentation (OSDI) (2024)

Reference 1

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Observation 9c1fef7d-b109-4af8-80d3-0846055aacfd · outbound

This paper cites In: Conference on Machine Learning and Systems (MLSys).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: Conference on Machine Learning and Systems (MLSys)

Reference 2

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Observation 46d8866b-546e-445f-b2ce-aa3684ee87a9 · outbound

This paper cites Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 3

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Observation fe4dde01-8498-4d92-89fc-a8761eccccde · outbound

This paper cites Efficient Heterogeneous Large Language Model Decoding with Model-Attention Disaggregation.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Efficient Heterogeneous Large Language Model Decoding with Model-Attention Disaggregation

Reference 4

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Observation 0eb80135-b49c-4da9-ab45-b2dba5d46348 · outbound

This paper cites Journal of Machine Learning Research24(240) (2023).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Journal of Machine Learning Research24(240) (2023)

Reference 5

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Observation c6057737-b21e-4a31-acc2-55d43c33af5c · outbound

This paper cites Zenodo (2024),https://doi.org/10.5281/zenodo.11171501.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Zenodo (2024),https://doi.org/10.5281/zenodo.11171501

Reference 6

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

Reference 7

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Observation 5e7c425e-9cf5-4e10-ac35-a024e27d7583 · outbound

This paper cites Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training

Reference 8

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Observation 6cd1d12e-9707-4b7f-bd59-8b17161fdf89 · outbound

This paper cites LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences

Reference 9

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This paper cites White paper, Google (2024),https://datacenters.google/efficiency/.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations White paper, Google (2024),https://datacenters.google/efficiency/

Reference 10

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

Reference 11

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Observation 2d94c17a-72d1-4440-a735-0e2d968b6fcc · outbound

This paper cites In: USENIX Symposium on Networked Systems Design and Imple- mentation (NSDI) (2024).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: USENIX Symposium on Networked Systems Design and Imple- mentation (NSDI) (2024)

Reference 12

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This paper cites In: ACM Symposium on Operating Systems Principles (SOSP) (2023).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: ACM Symposium on Operating Systems Principles (SOSP) (2023)

Reference 13

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Observation de4f7b80-4e9d-4b14-9037-82cc89224020 · outbound

This paper cites In: 2024 Conference on Empirical Methods in Nat- ural Language Processing (EMNLP) (2024).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: 2024 Conference on Empirical Methods in Nat- ural Language Processing (EMNLP) (2024)

Reference 14

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Observation 82f19ffe-d45d-4e31-85bd-68f6e37ec33f · outbound

This paper cites EcoServe: Designing Carbon-Aware AI Inference Systems.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations EcoServe: Designing Carbon-Aware AI Inference Systems

Reference 15

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

Reference 16

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Observation 403de67b-2ac1-4010-9fbb-c81f470a723c · outbound

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

Reference 17

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

Reference 18

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This paper cites In: ACM International Conference on Architectural Support for Program- ming Languages and Operating Systems (ASPLOS) (2024).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: ACM International Conference on Architectural Support for Program- ming Languages and Operating Systems (ASPLOS) (2024)

Reference 19

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Observation d4de1b5f-2adc-4662-82f1-9e0920f3c1f5 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Carbon Emissions and Large Neural Network Training

Reference 20

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This paper cites In: IEEE High Performance Extreme Computing Con- ference (HPEC) (2023).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: IEEE High Performance Extreme Computing Con- ference (HPEC) (2023)

Reference 21

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

Reference 24

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

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Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations Unresolved cited work

Reference 26

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Observation 055fbc76-5aec-4660-8892-d20f0afe872c · outbound

This paper cites In: Workshop on Sustainable Computer Systems (HotCarbon) (2024).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: Workshop on Sustainable Computer Systems (HotCarbon) (2024)

Reference 27

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This paper cites In: ACM International Conference on Future and Sustainable Energy Systems (e- Energy) (2025).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: ACM International Conference on Future and Sustainable Energy Systems (e- Energy) (2025)

Reference 28

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Observation 52bc0dec-2171-4a21-9bd4-b9c7cbc3de30 · outbound

This paper cites In: Conference on Machine Learning and Systems (MLSys) (2022).

Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations In: Conference on Machine Learning and Systems (MLSys) (2022)

Reference 29

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Pith citing papers

Observation f9b8a44b-1aac-4ce9-80e7-41d9b637682d · inbound

LLM Harms: A Taxonomy and Discussion cites this paper.

LLM Harms: A Taxonomy and Discussion Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

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Observation ee2f8263-d029-48a7-91ae-7a8cde0ccf88 · inbound

LLM Harms: A Taxonomy and Discussion cites this paper.

LLM Harms: A Taxonomy and Discussion Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 58

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Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation cites this paper.

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 28

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KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving cites this paper.

KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 47

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Observation f6a82cc8-99b0-47ef-91d6-195ca17d192c · inbound

LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites cites this paper.

LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 46

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Observation 9a2b2a88-b7a7-42dd-92c0-fd727608bb25 · inbound

LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites cites this paper.

LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 48

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Observation 9bc5a5d8-8331-4895-9847-593ff7682753 · inbound

Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale cites this paper.

Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 27

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Observation e2d013ff-5306-4e6c-9b9a-f188355c99d3 · inbound

Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale cites this paper.

Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 27

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Observation 6d6862fa-e65f-4b89-bffb-93385ca16e30 · inbound

EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving cites this paper.

EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

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Observation fa02a93d-fb18-485a-9a10-16f5b808f262 · inbound

EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving cites this paper.

EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 6

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Observation 5b02dda8-3851-4b52-9cda-7a338bfe53c3 · inbound

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs cites this paper.

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:47:40.829080Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.