Paper Citation Record · LEDGER

A Survey on Inference Optimization Techniques for Mixture of Experts Models

As of 21 August 2026, this Paper Citation Record lists 100 of 233 outbound references and 13 inbound Pith citation observations for arXiv:2412.14219.

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

pith.paper-citation-record.v1
2412.14219 v2

Coverage vector

measured 100 of 233 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:44:35.737640Z

measured 113 of 113 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:49:09.991963Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T17:14:57.408873Z

Reference resolution

100 of 233 outbound references displayed

verified exact0
verified fuzzy0
unresolved99
parse uncertain1
malformed identifier0
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External citation measurements

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Outbound references

Observation 75556786-36ec-49de-8532-1b590561cf25 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.073722Z

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Observation 819cb0be-ee3f-4339-b017-737378d4e2a6 · outbound

This paper cites On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes.

A Survey on Inference Optimization Techniques for Mixture of Experts Models On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.079132Z

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Observation b5160ad1-ab34-4960-b716-80ed71cb2eab · outbound

This paper cites A., Jin, H., and Wu, Y.

A Survey on Inference Optimization Techniques for Mixture of Experts Models A., Jin, H., and Wu, Y

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.083638Z

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Observation 8e7a7fe5-b759-4f60-8dff-b0c56a332909 · outbound

This paper cites Pytorch, 2024.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Pytorch, 2024

Reference 4

Resolution: parse uncertain; no resolver link, observed 2026-08-11T12:44:35.088670Z

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Observation 4da7fc1f-b00d-47ef-bf27-42088972725b · outbound

This paper cites M., Neyshabur, B., and Zhai, X.

A Survey on Inference Optimization Techniques for Mixture of Experts Models M., Neyshabur, B., and Zhai, X

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.092783Z

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Observation cc36abf9-4e7d-4445-ab1a-51e67f2edc21 · outbound

This paper cites Green ai: A preliminary empirical study on energy consumption in dl models across different runtime infrastructures.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Green ai: A preliminary empirical study on energy consumption in dl models across different runtime infrastructures

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.097360Z

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Observation ae11bb5a-b37c-4c47-b240-d9d4e73da5e6 · outbound

This paper cites Artificial analysis llm performance leaderboard, 2024.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Artificial analysis llm performance leaderboard, 2024

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.101848Z

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Observation 9963d321-045c-45cf-a97d-14c7a15f1a7f · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku, 2023.

A Survey on Inference Optimization Techniques for Mixture of Experts Models The claude 3 model family: Opus, sonnet, haiku, 2023

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.105844Z

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Observation 8d2b4150-d179-4f3f-9f0b-af6a917fc177 · outbound

This paper cites Efficient Large Scale Language Modeling with Mixtures of Experts.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Efficient Large Scale Language Modeling with Mixtures of Experts

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.109794Z

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Observation 7cd5cc8c-12a3-4e42-97fc-6d93e7143ee9 · outbound

This paper cites Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.114286Z

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Observation 48aa5ea5-c10a-4959-903e-ba1ad3bf2ca5 · outbound

This paper cites E., Akella, A., and Wang, Z.Read-me: Refactorizing llms as router-decoupled mixture of experts with system co-design, 2024.

A Survey on Inference Optimization Techniques for Mixture of Experts Models E., Akella, A., and Wang, Z.Read-me: Refactorizing llms as router-decoupled mixture of experts with system co-design, 2024

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.119325Z

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Observation ee4a0f25-b9cf-4e47-a5ba-819afc92b3e6 · outbound

This paper cites Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.123801Z

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Observation 21065e8d-4f60-429c-892d-b9447403fd4b · outbound

This paper cites Authorea Preprints (2024).

A Survey on Inference Optimization Techniques for Mixture of Experts Models Authorea Preprints (2024)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.128321Z

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Observation 704bd528-17da-4d98-8598-51a5d1566ea6 · outbound

This paper cites Moc-system: Efficient fault tolerance for sparse mixture-of-experts model training, 2024.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Moc-system: Efficient fault tolerance for sparse mixture-of-experts model training, 2024

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.132287Z

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Observation f96f75df-b905-4a1c-b369-d7f6a433b973 · outbound

This paper cites E., Zaharia, M., and Stoica, I.Moe-lightning: High-throughput moe inference on memory-constrained gpus, 2024.

A Survey on Inference Optimization Techniques for Mixture of Experts Models E., Zaharia, M., and Stoica, I.Moe-lightning: High-throughput moe inference on memory-constrained gpus, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.135870Z

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Observation 877d352a-259b-4c49-8629-2767b2fa037a · outbound

This paper cites Advances in Neural Information Processing Systems 35 (2022), 22173–22186.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Advances in Neural Information Processing Systems 35 (2022), 22173–22186

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.139850Z

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Observation f9bbfdc1-2141-4094-852c-6aed723071c1 · outbound

This paper cites arXiv preprint arXiv:2410.08589 (2024).

A Survey on Inference Optimization Techniques for Mixture of Experts Models arXiv preprint arXiv:2410.08589 (2024)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.143261Z

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Observation 0b99f528-462e-49d7-a05f-8956a01c7f87 · outbound

This paper cites Task-Specific Expert Pruning for Sparse Mixture-of-Experts.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Task-Specific Expert Pruning for Sparse Mixture-of-Experts

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.146689Z

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Observation cece13b7-bdb5-4c09-937c-f048a482fcd1 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Reproducible scaling laws for contrastive language-image learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.150268Z

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Observation 5623aee1-4c4a-42ae-b2c1-c7cb374a55bf · outbound

This paper cites W., Sutton, C., Gehrmann, S., et al.

A Survey on Inference Optimization Techniques for Mixture of Experts Models W., Sutton, C., Gehrmann, S., et al

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.154201Z

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Observation 3f4913c2-b64e-48ee-ac41-693744a62138 · outbound

This paper cites A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts.

A Survey on Inference Optimization Techniques for Mixture of Experts Models A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.157741Z

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Observation f6d64cac-06c8-4f90-8841-87a63ac5d28b · outbound

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.177680Z

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Observation 5a5ecbdb-2329-480a-b43a-f438a6398fc7 · outbound

This paper cites Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.197818Z

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Observation 3f3ce8e0-35e7-459a-aedd-c8bbdb309866 · outbound

This paper cites No Language Left Behind: Scaling Human-Centered Machine Translation.

A Survey on Inference Optimization Techniques for Mixture of Experts Models No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.220112Z

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Observation 9e132ec2-57b1-428c-aee1-b7347f94442f · outbound

This paper cites Innovating for Tomorrow: The Convergence of SE and Green AI.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Innovating for Tomorrow: The Convergence of SE and Green AI

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.240011Z

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Observation f8d5e531-5c6e-4610-bf94-d5320e8c1a65 · outbound

This paper cites MoEUT: Mixture-of-Experts Universal Transformers.

A Survey on Inference Optimization Techniques for Mixture of Experts Models MoEUT: Mixture-of-Experts Universal Transformers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.256899Z

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Observation f7c9e1aa-fa4d-4b21-b550-578058234edc · outbound

This paper cites SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention.

A Survey on Inference Optimization Techniques for Mixture of Experts Models SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.261587Z

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Observation 8e3e9628-ed37-4f82-83a3-b272df83d33d · outbound

This paper cites In 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23) (Boston, MA, July 2023), USENIX Association, pp.

A Survey on Inference Optimization Techniques for Mixture of Experts Models In 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23) (Boston, MA, July 2023), USENIX Association, pp

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.265805Z

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Observation 07d6e292-912b-40ff-b8dc-9497e9e85446 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

A Survey on Inference Optimization Techniques for Mixture of Experts Models DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.269913Z

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Observation f92c5aff-79a8-4c33-a1f6-63447148a713 · outbound

This paper cites Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.273834Z

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Observation f571f914-c158-4d77-bf54-3a425daa95a5 · outbound

This paper cites DeepSeek-V3 Technical Report.

A Survey on Inference Optimization Techniques for Mixture of Experts Models DeepSeek-V3 Technical Report

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.277153Z

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Observation 64aa3727-b185-4784-90da-1035a4137392 · outbound

This paper cites an unresolved cited work.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Unresolved cited work

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.280993Z

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Observation 2d8dade5-e848-4ab7-9560-8805a61075f9 · outbound

This paper cites XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts.

A Survey on Inference Optimization Techniques for Mixture of Experts Models XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.284328Z

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Observation 4e9f4672-1f2a-4e46-8234-04387efb75f6 · outbound

This paper cites Automatically constructing a corpus of sentential paraphrases.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Automatically constructing a corpus of sentential paraphrases

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.287945Z

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Observation d0b204e6-0e98-410c-831c-8daaa84627e7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

A Survey on Inference Optimization Techniques for Mixture of Experts Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.291549Z

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Observation 880ad4af-6edb-489f-a983-bea0e73d20ac · outbound

This paper cites In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2024), pp.

A Survey on Inference Optimization Techniques for Mixture of Experts Models In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2024), pp

Reference 36

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Observation 345194ac-4b9c-430e-9258-7cbb7fae0aec · outbound

This paper cites M., Tong, S., Lepikhin, D., Xu, Y., Krikun, M., Zhou, Y., Yu, A.

A Survey on Inference Optimization Techniques for Mixture of Experts Models M., Tong, S., Lepikhin, D., Xu, Y., Krikun, M., Zhou, Y., Yu, A

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.298507Z

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Observation 31d55731-a4aa-4ede-be1f-31f70cc041cd · outbound

This paper cites Sida: Sparsity-inspired data-aware serving for efficient and scalable large mixture-of-experts models.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Sida: Sparsity-inspired data-aware serving for efficient and scalable large mixture-of-experts models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.301772Z

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Observation 47a8d856-94e5-490b-ae52-07e4d6c84349 · outbound

This paper cites Learning Factored Representations in a Deep Mixture of Experts.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Learning Factored Representations in a Deep Mixture of Experts

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.305018Z

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Observation d6be3993-e01f-4bed-890f-9fe9708749d7 · outbound

This paper cites Fast Inference of Mixture-of-Experts Language Models with Offloading.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Fast Inference of Mixture-of-Experts Language Models with Offloading

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.308441Z

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Observation a15d8e24-8217-467a-a609-7ddcea6ce950 · outbound

This paper cites LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models.

A Survey on Inference Optimization Techniques for Mixture of Experts Models LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 41

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Observation cb5e4eb4-18b4-497d-b3f5-1a9580d3f5b5 · outbound

This paper cites Advances in Neural Information Processing Systems 35 (2022), 28441–28457.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Advances in Neural Information Processing Systems 35 (2022), 28441–28457

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.315464Z

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Observation 0c6aa6d4-bf5e-4d24-b3f3-c390eefc1c35 · outbound

This paper cites A Review of Sparse Expert Models in Deep Learning.

A Survey on Inference Optimization Techniques for Mixture of Experts Models A Review of Sparse Expert Models in Deep Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.318591Z

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Observation 077fe38b-bb39-4943-87f0-b24f7bb77761 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 44

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Observation 9a04c506-e79c-40b4-8b5b-3dab7c107b41 · outbound

This paper cites QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models.

A Survey on Inference Optimization Techniques for Mixture of Experts Models QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Reference 45

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Observation 9ddd0bcc-00e8-4496-8648-877579f65487 · outbound

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

A Survey on Inference Optimization Techniques for Mixture of Experts Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.412088Z

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Observation 47017505-543e-43c8-bbd3-000cbc5ac293 · outbound

This paper cites arXiv preprint arXiv:2412.07067 (2024).

A Survey on Inference Optimization Techniques for Mixture of Experts Models arXiv preprint arXiv:2412.07067 (2024)

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.441234Z

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Observation 934038ce-097d-4c6b-a4dd-685fe481eb6b · outbound

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

A Survey on Inference Optimization Techniques for Mixture of Experts Models LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.444971Z

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Observation 8d1e3641-3c36-4981-96da-d70770bcdfdb · outbound

This paper cites Higher Layers Need More LoRA Experts.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Higher Layers Need More LoRA Experts

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.448889Z

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Observation 1707126b-88c3-4a63-ae03-3432f0526543 · outbound

This paper cites Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.452797Z

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Observation f9dc8566-d88f-4f27-8b79-5f3a0d5e918b · outbound

This paper cites Llama.cpp, 2023.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Llama.cpp, 2023

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.457032Z

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Observation 6b57d0ed-92c0-43bb-b6e9-8e5a7681d2a5 · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

A Survey on Inference Optimization Techniques for Mixture of Experts Models MiniLLM: On-Policy Distillation of Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.460526Z

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Observation b0867ddf-9f69-4902-9cce-0fc23997bd68 · outbound

This paper cites Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.464375Z

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Observation 4fd36e36-9173-4575-b923-fbe5f112f3c0 · outbound

This paper cites Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.467652Z

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A Survey on Inference Optimization Techniques for Mixture of Experts Models IEEE Transactions on Pattern Analysis and Machine Intelligence 14 , 7 (1992), 751–769

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A Survey on Inference Optimization Techniques for Mixture of Experts Models FastMoE: A Fast Mixture-of-Expert Training System

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A Survey on Inference Optimization Techniques for Mixture of Experts Models In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (New York, NY, USA, 2022), PPoPP ’22, Association for Computing Machinery, p

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Merging Experts into One: Improving Computational Efficiency of Mixture of Experts

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Y.Expertflow: Optimized expert activation and token allocation for efficient mixture-of-experts inference

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Measuring Massive Multitask Language Understanding

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Improving efficiency in multi-modal autonomous embedded systems through adaptive gating

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Mixture Compressor for Mixture-of-Experts LLMs Gains More

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Experts Weights Averaging: A New General Training Scheme for Vision Transformers

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Llm-perf leaderboard, 2023

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Transformers: State-of-the-art machine learning for jax, pytorch and tensorflow, 2023

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Proceedings of Machine Learning and Systems 5 (2023), 269–287

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A Survey on Inference Optimization Techniques for Mixture of Experts Models In 2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) (2024), IEEE, pp

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Mixture of Experts with Mixture of Precisions for Tuning Quality of Service

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Beyond data and model parallelism for deep neural networks

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Mixtral of Experts

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A Survey on Inference Optimization Techniques for Mixture of Experts Models MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

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This paper cites arXiv preprint arXiv:2410.11842 (2024).

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Scaling Laws for Neural Language Models

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A Survey on Inference Optimization Techniques for Mixture of Experts Models In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (2018), pp

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A Survey on Inference Optimization Techniques for Mixture of Experts Models LaDiMo: Layer-wise Distillation Inspired MoEfier

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Monde: Mixture of near-data experts for large-scale sparse models

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production

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A Survey on Inference Optimization Techniques for Mixture of Experts Models SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Learning multiple layers of features from tiny images

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A Survey on Inference Optimization Techniques for Mixture of Experts Models H., Gonzalez, J

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A Survey on Inference Optimization Techniques for Mixture of Experts Models STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning

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A Survey on Inference Optimization Techniques for Mixture of Experts Models GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Base layers: Simplifying training of large, sparse models

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A Survey on Inference Optimization Techniques for Mixture of Experts Models LLM Inference Serving: Survey of Recent Advances and Opportunities

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A Survey on Inference Optimization Techniques for Mixture of Experts Models In 2023 USENIX Annual Technical Conference (USENIX ATC 23) (Boston, MA, July 2023), USENIX Association, pp

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Adaptive Gating in Mixture-of-Experts based Language Models

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A Survey on Inference Optimization Techniques for Mixture of Experts Models LocMoE: A Low-Overhead MoE for Large Language Model Training

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Optimizing Mixture-of-Experts Inference Time Combining Model Deployment and Communication Scheduling

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective

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A Survey on Inference Optimization Techniques for Mixture of Experts Models Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models

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A Survey on Inference Optimization Techniques for Mixture of Experts Models QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts

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Observation 626f439a-51d7-48d7-9930-d0c856a573a6 · inbound

CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution cites this paper.

CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution A Survey on Inference Optimization Techniques for Mixture of Experts Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-08-11T02:23:21.063212Z

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Observation c389cbba-d360-4036-8a3b-84c8ab9d75bd · inbound

Beyond Uniform Experts: Cost-Aware Expert Execution for Efficient Multi-Device MoE Inference cites this paper.

Beyond Uniform Experts: Cost-Aware Expert Execution for Efficient Multi-Device MoE Inference A Survey on Inference Optimization Techniques for Mixture of Experts Models

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-08-11T02:23:21.063212Z

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Observation 8f2b9ad3-4bc8-440b-b148-5692f207abeb · inbound

The Evolution of Mixture-of-Experts Architectures in Large Language Models: Routing, Topology, Load Balancing, and Expert Parallelism cites this paper.

The Evolution of Mixture-of-Experts Architectures in Large Language Models: Routing, Topology, Load Balancing, and Expert Parallelism A Survey on Inference Optimization Techniques for Mixture of Experts Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-14T04:32:58.262836Z

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