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Paper Citation Record · LEDGER

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT

As of 19 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2601.20408.

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

pith.paper-citation-record.v1
2601.20408 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:43:40.034740Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

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  • verified fuzzy3
  • unresolved17
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  • metadata mismatch1

External citation measurements

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Outbound references

Observation db620c7d-7f74-4040-b393-cbad5dc1bbbe · outbound

This paper cites SCOOT: SLO-Oriented Performance Tuning for LLM Inference Engines.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT SCOOT: SLO-Oriented Performance Tuning for LLM Inference Engines

Reference 4

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source=pdf_text observed=2026-08-15T15:43:39.740486Z digest=sha256:4d7696c8b101e169c8cd1de3a3a263ad1217b7ac63d091cd0cf15581bed02129

Observation dc86fafa-233b-47d3-a33b-b420c91fcf27 · outbound

This paper cites Values are normalized per-GPU RPS (SLO- compliant).

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Values are normalized per-GPU RPS (SLO- compliant)

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T15:43:40.031020Z digest=sha256:bd5b83ed984519ac392e84c883687f25967fc7c02ee8a60a8cb35679292d70d6

Observation 5fa9a87e-09d6-4ab2-8248-764f735a30d1 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 7

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source=pdf_text observed=2026-08-15T15:43:39.752107Z digest=sha256:cd321bf2fc75ccbf016f07bae21b748284ab473d48b5a54ea46c805a2a39a1cd

Observation 7a58d578-000b-4b15-8ac0-ead3955c3490 · outbound

This paper cites FP8 Formats for Deep Learning.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT FP8 Formats for Deep Learning

Reference 8

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source=pdf_text observed=2026-08-15T15:43:39.774740Z digest=sha256:dc836a89487c615872c7a23c8c44c7c89ac1ef18fb29a2b2ba14eb1ae540757f

Observation 7aca0f86-6382-4244-b211-bb5a32d3cdec · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Accelerating Sparse Deep Neural Networks

Reference 9

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source=pdf_text observed=2026-08-15T15:43:39.831138Z digest=sha256:a41073fb1c04730b33bb48cb140498d0fc4fec4bced1d8dd5523536946fa8430

Observation a53b15aa-46dc-4f40-8945-683477a383ed · outbound

This paper cites Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT NVIDIA Corporation.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT NVIDIA Corporation

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T15:43:39.935237Z digest=sha256:4fb5da45d1b6e83eff97354bd6593cc63bea75d651a2313819307acf4df62db6

Observation b168973b-90f7-4e24-8165-29ba09525c8c · outbound

This paper cites Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs

Reference 11

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source=pdf_text observed=2026-08-15T15:43:39.999023Z digest=sha256:a405d9e8445101f4921e8efed3c8fcf7421416a5223ebdb6b364b6bd61e12670

Observation 41e6031f-1b1c-4d64-8de8-6c1363ddbe83 · outbound

This paper cites A survey on inference engines for large language mod- els: Perspectives on optimization and efficiency.arXiv preprint arXiv:2505.01658,.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT A survey on inference engines for large language mod- els: Perspectives on optimization and efficiency.arXiv preprint arXiv:2505.01658,

Reference 12

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source=pdf_text observed=2026-08-15T15:43:40.003167Z digest=sha256:e94044ff57b332f3d26116e32bc7e366c48ca05e9e638f1410d616983935fb41

Observation 183ffb50-63f3-45e4-a35c-21993c227f22 · outbound

This paper cites Qwen2.5 Technical Report.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Qwen2.5 Technical Report

Reference 13

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source=pdf_text observed=2026-08-15T15:43:40.006956Z digest=sha256:1ed3fe7e41a03e4d1aefca7a15a463c1df0caa32dfa6c0610d993159c017adb8

Observation 2306c056-9a67-424b-802e-e303b40a6da9 · outbound

This paper cites Model Compression and Efficient Inference for Large Language Models: A Survey.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Model Compression and Efficient Inference for Large Language Models: A Survey

Reference 14

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source=pdf_text observed=2026-08-15T15:43:40.010677Z digest=sha256:3a7f3704166b46f66054219dc5a1bee285d424b2e94833124b9e1a893403e283

Observation 41730741-cd6e-472f-a00c-7fdfbb9490b4 · outbound

This paper cites On the Impact of Calibration Data in Post-training Quantization and Pruning.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT On the Impact of Calibration Data in Post-training Quantization and Pruning

Reference 15

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source=pdf_text observed=2026-08-15T15:43:40.014564Z digest=sha256:c073e2f0f121c0d83f17528d87b0eeb6c024f6f8ed0e21154f051ebbdb84a372

Observation 39c01b4e-163c-4b5e-adf0-7200a49278c6 · outbound

This paper cites High-Throughput LLM inference on Heterogeneous Clusters.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT High-Throughput LLM inference on Heterogeneous Clusters

Reference 16

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local_arxiv, observed 2026-08-15T15:43:40.100630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T15:43:40.017881Z digest=sha256:b71a0681f30ca30bc92b4e7854d2d66d8df5ad59b5b69bfad77d9ddc3bc770f2

Observation b8329d69-3ab0-4e7d-9cc0-4ba684ce9bfc · outbound

This paper cites Taming the Titans: A Survey of Efficient LLM Inference Serving.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Taming the Titans: A Survey of Efficient LLM Inference Serving

Reference 17

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source=pdf_text observed=2026-08-15T15:43:40.020798Z digest=sha256:de90d8c94a35b7e1716f290a52e07ce828f8eb36e40d42cfe3ba8f7e13d10649

Observation f6e18d0b-5288-4ca0-9daf-fc96008c04ef · outbound

This paper cites SGLang: Efficient Execution of Structured Language Model Programs.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT SGLang: Efficient Execution of Structured Language Model Programs

Reference 18

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source=pdf_text observed=2026-08-15T15:43:40.024288Z digest=sha256:ce4519e0893b25db68ea2306f64071419fd5f913856a0e20914456f42d555d04

Observation 576d568f-9d7f-4671-b22f-9c26a43edb38 · outbound

This paper cites A Survey on Efficient Inference for Large Language Models.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT A Survey on Efficient Inference for Large Language Models

Reference 19

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source=pdf_text observed=2026-08-15T15:43:40.027135Z digest=sha256:de16c8da760e0dd33c33652031cd636ab439bc2a61ad6627c0edb320cf272876

Observation 5e838d80-e8f9-4693-87c2-6e7ad83e05dc · outbound

This paper cites FP16 baseline across models, tensor parallelism, and bitwidths.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT FP16 baseline across models, tensor parallelism, and bitwidths

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T15:43:40.034740Z digest=sha256:b28d54d5b1ecd0d967bf2b56476d6987e097918acc55465bdc97aee18c2aac7b

Observation 271f6761-79f9-4155-99ad-c7bd0d925bb8 · outbound

This paper cites Language Models are Few-Shot Learners.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Language Models are Few-Shot Learners

Reference 2020

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source=pdf_text observed=2026-08-15T15:43:39.735588Z digest=sha256:0ed55f2b505b5f9c60e12df0459f186643949151bed0bdfc6578cb6006e7ab79

Observation b24b7d7e-40e5-464e-88b1-ac0fa8935649 · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 2021

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source=pdf_text observed=2026-08-15T15:43:39.744744Z digest=sha256:d731cb2b5bad32e75c2d52f56368882abeebb5a08ca249a0e75481dc4b126ffc

Observation de2024b6-7be4-4441-b023-1ac803709582 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2023

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source=pdf_text observed=2026-08-15T15:43:39.748630Z digest=sha256:a505caebb1eaf02eb39a7dbfb0989765257b67446c8eaffcbdb5b9c14a5617a8

Observation e892688b-f940-409e-8afb-5464599442eb · outbound

This paper cites The Llama 3 Herd of Models.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT The Llama 3 Herd of Models

Reference 2024

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source=pdf_text observed=2026-08-15T15:43:39.589707Z digest=sha256:3f68363519c3abb0377de15356477e2388ac079d3b5327aad2dde7f713907d4b

Observation 55f68562-cf37-47ed-a205-760a352ca389 · outbound

This paper cites Qwen3 Technical Report.

Meeting SLOs, Slashing Hours: Automated Enterprise LLM Optimization with OptiKIT Qwen3 Technical Report

Reference 2025

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source=pdf_text observed=2026-08-15T15:43:39.686068Z digest=sha256:3289bcc81198e3b7c3f841f62bf98d7c529f8441c4061c5d461619c14455bf1c

Pith citing papers

No inbound Pith citation observations are available.