FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models | Paper Citation Record

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This paper cites https://developer.nvidia.com/blog/doubling-all2all- performance-with-nvidia-collective-communication-library-2-12/.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models https://developer.nvidia.com/blog/doubling-all2all- performance-with-nvidia-collective-communication-library-2-12/

Reference 1

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This paper cites Deepspeed-inference: enabling efficient infer- ence of transformer models at unprecedented scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepspeed-inference: enabling efficient infer- ence of transformer models at unprecedented scale

Reference 2

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Observation a8bf19b0-5037-4016-a15e-e11f7b484f37 · outbound

This paper cites Language models are few-shot learners.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Language models are few-shot learners

Reference 3

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This paper cites FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion

Reference 4

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This paper cites Centauri: Enabling efficient sched- uling for communication-computation overlap in large model train- ing via communication partitioning.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Centauri: Enabling efficient sched- uling for communication-computation overlap in large model train- ing via communication partitioning

Reference 5

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Observation 2c3471e6-22bb-4817-a501-5d26b5371634 · outbound

This paper cites On the representation collapse of sparse mixture of experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models On the representation collapse of sparse mixture of experts

Reference 6

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Observation 91b8be49-ef8e-47b9-87da-b812022e2b72 · outbound

This paper cites Palm: Scaling language modeling with pathways.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Palm: Scaling language modeling with pathways

Reference 7

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Observation 96093689-98ff-4c7c-82c8-acc315e8ae4f · outbound

This paper cites Stablemoe: Stable routing strategy for mixture of experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Stablemoe: Stable routing strategy for mixture of experts

Reference 8

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Observation a9ed7357-2285-4a41-83fc-f6ff33610287 · outbound

This paper cites Large scale distributed deep networks.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Large scale distributed deep networks

Reference 9

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Observation 27b22620-c9b6-4b58-bc38-3cf0ad4ac1fd · outbound

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

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024

Reference 10

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This paper cites PaLM-E: An Embodied Multimodal Language Model.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models PaLM-E: An Embodied Multimodal Language Model

Reference 11

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

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 12

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This paper cites FastMoE: A Fast Mixture-of-Expert Training System.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FastMoE: A Fast Mixture-of-Expert Training System

Reference 13

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This paper cites FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models

Reference 14

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This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 15

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This paper cites Experts Weights Averaging: A New General Training Scheme for Vision Transformers.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 16

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Observation 634bc3ae-50d3-473e-a721-10f5ad0a058d · outbound

This paper cites Tutel: Adaptive mixture-of-experts at scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Tutel: Adaptive mixture-of-experts at scale

Reference 17

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Observation 83246816-fbbf-4c31-8cf4-f1b71791528f · outbound

This paper cites Breaking the computation and communication abstraction barrier in distributed machine learning workloads.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Breaking the computation and communication abstraction barrier in distributed machine learning workloads

Reference 18

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This paper cites Highly scalable deep learning training system with mixed-precision: Training ImageNet in four minutes.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Highly scalable deep learning training system with mixed-precision: Training ImageNet in four minutes

Reference 19

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Observation e201b5e5-da61-4f10-9622-d1c5cf2c216a · outbound

This paper cites Mixtral of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Mixtral of Experts

Reference 20

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This paper cites Lancet: Accelerating mixture-of-experts training by over- lapping weight gradient computation and all-to-all communication.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Lancet: Accelerating mixture-of-experts training by over- lapping weight gradient computation and all-to-all communication

Reference 21

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Observation 339ab4a9-001b-4c33-a978-aa7fe1876c84 · outbound

This paper cites Gshard: Scaling giant models with conditional compu- tation and automatic sharding.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gshard: Scaling giant models with conditional compu- tation and automatic sharding

Reference 22

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This paper cites BASE layers: Simplifying training of large, sparse models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models BASE layers: Simplifying training of large, sparse models

Reference 23

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Observation 8fb7b99b-61d0-48b7-882b-b28145780031 · outbound

This paper cites Acceler- ating distributed{MoE} training and inference with lina.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Acceler- ating distributed{MoE} training and inference with lina

Reference 24

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Observation 71b22f36-7a78-4d02-b6df-3910757c8aae · outbound

This paper cites Janus: A unified dis- tributed training framework for sparse mixture-of-experts models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Janus: A unified dis- tributed training framework for sparse mixture-of-experts models

Reference 25

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This paper cites Gating dropout: Communication-efficient regularization for sparsely activated transformers.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gating dropout: Communication-efficient regularization for sparsely activated transformers

Reference 26

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This paper cites Modeling task relationships in multi-task learning with multi- gate mixture-of-experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Modeling task relationships in multi-task learning with multi- gate mixture-of-experts

Reference 27

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This paper cites Bagualu: targeting brain scale pretrained models with over 37 million cores.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Bagualu: targeting brain scale pretrained models with over 37 million cores

Reference 28

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This paper cites Efficient large- scale language model training on GPU clusters using Megatron-LM.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Efficient large- scale language model training on GPU clusters using Megatron-LM

Reference 29

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This paper cites Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement

Reference 30

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This paper cites HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System

Reference 31

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This paper cites Springer, 1999.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Springer, 1999

Reference 32

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This paper cites Parm: Efficient training of large sparsely-activated models with dedicated schedules.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Parm: Efficient training of large sparsely-activated models with dedicated schedules

Reference 33

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This paper cites Sinclair.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Sinclair

Reference 34

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This paper cites Differential evolution.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Differential evolution

Reference 35

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Observation 97fbd66a-aa5f-4700-a6e9-6a3ee8637a87 · outbound

This paper cites From Sparse to Soft Mixtures of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models From Sparse to Soft Mixtures of Experts

Reference 36

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This paper cites Beckmann.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Beckmann

Reference 37

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This paper cites Language models are unsupervised multitask learners.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Language models are unsupervised multitask learners

Reference 38

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This paper cites Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale

Reference 39

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This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 40

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This paper cites Exploiting simultaneous communications to accelerate data parallel distributed deep learning.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Exploiting simultaneous communications to accelerate data parallel distributed deep learning

Reference 41

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This paper cites PipeMoE: Ac- celerating mixture-of-experts through adaptive pipelining.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models PipeMoE: Ac- celerating mixture-of-experts through adaptive pipelining

Reference 42

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Observation 5ade87aa-a439-476f-97d5-c54b1ed5f19d · outbound

This paper cites Schemoe: An ex- tensible mixture-of-experts distributed training system with tasks scheduling.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Schemoe: An ex- tensible mixture-of-experts distributed training system with tasks scheduling

Reference 43

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This paper cites A hybrid tensor-expert- data parallelism approach to optimize mixture-of-experts training.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models A hybrid tensor-expert- data parallelism approach to optimize mixture-of-experts training

Reference 44

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Observation e9e45f2c-f3cd-4126-b2c9-30360a6bdf08 · outbound

This paper cites Attention is all you need.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Attention is all you need

Reference 45

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This paper cites Overlap communication with dependent compu- tation via decomposition in large deep learning models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Overlap communication with dependent compu- tation via decomposition in large deep learning models

Reference 46

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Observation d8952115-49a6-4eb3-8d5f-a75eb3d24060 · outbound

This paper cites Large batch optimization for deep learning: Training BERT in 76 minutes.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Large batch optimization for deep learning: Training BERT in 76 minutes

Reference 47

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This paper cites SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts

Reference 48

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This paper cites SmartMoE: Efficiently training Sparsely-Activated mod- els through combining offline and online parallelization.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models SmartMoE: Efficiently training Sparsely-Activated mod- els through combining offline and online parallelization

Reference 49

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Observation d9c12ac6-24df-4160-81c0-2d54e0dd9b73 · outbound

This paper cites Pit: Optimization of dynamic sparse deep learning models via permutation invariant transformation.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Pit: Optimization of dynamic sparse deep learning models via permutation invariant transformation

Reference 50

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Observation 487fa7b9-974a-45bc-9f95-830f85be3280 · outbound

This paper cites Mixture-of-Experts with Expert Choice Routing.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Mixture-of-Experts with Expert Choice Routing

Reference 51

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Observation f4b27ede-16fc-4098-bbbe-a828271fcf63 · outbound

This paper cites Taming sparsely activated transformer with stochastic experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Taming sparsely activated transformer with stochastic experts

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.423266Z

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