Paper Citation Record · LEDGER

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

As of 6 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 55 inbound Pith citation observations for arXiv:2201.11990.

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

pith.paper-citation-record.v1
2201.11990 v3

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

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measured 133 of 133 standing notices

One-hop event checks from named stored sources.

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measured 55 of 55 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:53:13.792040Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

78 of 78 outbound references displayed

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External citation measurements

299
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation c4a63d83-df42-47fd-aedc-fa7568234c1f · outbound

This paper cites https://www.nvidia.com/en-us/data-center/a100/.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model https://www.nvidia.com/en-us/data-center/a100/

Reference 1

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Observation 4ddbc2dc-1fce-44b4-8cee-8c4ab5c08f44 · outbound

This paper cites https://www.top500.org/system/179842/.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model https://www.top500.org/system/179842/

Reference 2

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Observation 5683aae2-8d70-469f-b2cf-48b660dc388c · outbound

This paper cites https://www.nvidia.com/en-us/data-center/nvlink/.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model https://www.nvidia.com/en-us/data-center/nvlink/

Reference 3

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Observation ec0bffb2-6f4a-440d-9eb8-f509b0b8dfff · outbound

This paper cites https://www.microsoft.com/en-us/research/blog/ turing-nlg-a-17-billion-parameter-language-model-by-microsoft/.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model https://www.microsoft.com/en-us/research/blog/ turing-nlg-a-17-billion-parameter-language-model-by-microsoft/

Reference 4

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Observation ad2fdb52-dd7d-4acc-a816-91e5e9f271bb · outbound

This paper cites https://wudaoai.cn/home.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model https://wudaoai.cn/home

Reference 5

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Observation 5301a898-11bc-4d8e-bede-e78fe10659a5 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Piqa: Reasoning about physical commonsense in natural language

Reference 6

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Observation 061dbb82-7aa5-433c-b3c6-f8ce0a0c28f4 · outbound

This paper cites Zou, Venkatesh Saligrama, and Adam Tauman Kalai.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Zou, Venkatesh Saligrama, and Adam Tauman Kalai

Reference 7

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Observation c3e9e4bd-8b95-4171-85e9-f3bfb7104923 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model On the Opportunities and Risks of Foundation Models

Reference 8

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Unresolved cited work

Reference 9

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Observation 40fc54e9-1170-48a4-99a6-4c7894c4bedf · outbound

This paper cites BoolQ: Exploring the surprising difﬁculty of natural yes/no questions.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model BoolQ: Exploring the surprising difﬁculty of natural yes/no questions

Reference 10

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Observation 0b3b913b-a9c5-48f8-8f97-1a96a0bc2af1 · outbound

This paper cites langdetect.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model langdetect

Reference 11

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 12

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Observation 6bedb14c-37e8-4b98-9fc1-23b6561965bf · outbound

This paper cites Language and Gender.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Language and Gender

Reference 13

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Observation 0a2cab10-6e8a-4661-aaeb-31438aa8795f · outbound

This paper cites Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting

Reference 14

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Observation 188c7c7c-c2a4-4ac2-963e-06c2c914aecc · outbound

This paper cites Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Reference 15

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Unresolved cited work

Reference 16

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Observation 95c74833-2394-4f52-9d56-76c3bd30f954 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 17

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Observation 341446bc-7976-4cf0-a74f-82c12d1ad3f3 · outbound

This paper cites A framework for few-shot language model evaluation, September 2021.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model A framework for few-shot language model evaluation, September 2021

Reference 18

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Observation 453a68ec-41cd-486a-8e1f-f183bea12a38 · outbound

This paper cites Realtoxici- typrompts: Evaluating neural toxic degeneration in language models.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Realtoxici- typrompts: Evaluating neural toxic degeneration in language models

Reference 19

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Unresolved cited work

Reference 20

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This paper cites Bowman, and Noah A.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Bowman, and Noah A

Reference 21

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Observation 9ee7a11c-92cf-48bd-8e3d-fb43f4301d09 · outbound

This paper cites Pretrained transformers improve out-of-distribution robustness.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Pretrained transformers improve out-of-distribution robustness

Reference 22

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Observation 3cd25986-83c6-45e4-a23e-a24a8d18f49b · outbound

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

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Gpipe: Efﬁcient training of giant neural networks using pipeline parallelism

Reference 23

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Observation a6dcb3a6-f388-4e35-99d4-376a46e86c7a · outbound

This paper cites Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning

Reference 24

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Observation a077427a-6faf-4744-8c8f-9acfd78bdd1c · outbound

This paper cites Weld, and Luke Zettlemoyer.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Weld, and Luke Zettlemoyer

Reference 25

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Observation 6e4ad3f1-64b6-4807-a5da-19b94718b99b · outbound

This paper cites Exploring the Limits of Language Modeling.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Exploring the Limits of Language Modeling

Reference 26

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Adam: A Method for Stochastic Optimization

Reference 27

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This paper cites Gedi: Generative discriminator guided sequence generation.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Gedi: Generative discriminator guided sequence generation

Reference 28

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Observation 086efc00-3fc5-40d9-a52c-5b1a0567564b · outbound

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Dense-captioning events in videos

Reference 29

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Observation dcf2d155-6709-455d-a93f-61efb86e1fba · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 30

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This paper cites RACE: Large-scale ReAd- ing comprehension dataset from examinations.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model RACE: Large-scale ReAd- ing comprehension dataset from examinations

Reference 31

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Observation 7e4d6e39-99c8-4864-8525-f548a01cda0d · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 32

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Observation d3b7ae9c-cd6d-447f-87fd-05b91b1129f0 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 33

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Jurassic-1: Technical details and evaluation

Reference 34

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Observation 3f8851c0-dbbc-416f-9e39-fb38c754b508 · outbound

This paper cites M6: A Chinese Multimodal Pretrainer.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model M6: A Chinese Multimodal Pretrainer

Reference 35

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Observation 8a6709e2-8ed1-478c-90d0-66943b39cc94 · outbound

This paper cites M6-10t: A sharing-delinking paradigm for efﬁcient multi-trillion parameter pretraining.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model M6-10t: A sharing-delinking paradigm for efﬁcient multi-trillion parameter pretraining

Reference 36

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Observation 4eb41430-3e27-4438-a046-df04adb8ab8e · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 37

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Observation 5092cdbb-bd20-43ab-8fd5-6bf253f1598e · outbound

This paper cites Unicorn on rainbow: A universal commonsense reasoning model on a new multitask benchmark.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Unicorn on rainbow: A universal commonsense reasoning model on a new multitask benchmark

Reference 38

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Observation 5bc79588-2db3-4cdc-8f64-e82e16247f79 · outbound

This paper cites Black is to criminal as cau- casian is to police: Detecting and removing multiclass bias in word embeddings.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Black is to criminal as cau- casian is to police: Detecting and removing multiclass bias in word embeddings

Reference 39

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Observation 347773d3-4b81-4e67-a5de-1a5419679380 · outbound

This paper cites Right for the wrong reasons: Diagnosing syntactic heuris- tics in natural language inference.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Right for the wrong reasons: Diagnosing syntactic heuris- tics in natural language inference

Reference 40

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Observation 1a9df28b-c310-4be6-a43c-706ca4c966dc · outbound

This paper cites Mixed Precision Training.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Mixed Precision Training

Reference 41

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Observation 0751dc8d-27f2-4f36-8e07-e6b601f4b40c · outbound

This paper cites Pipedream: generalized pipeline parallelism for dnn training.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Pipedream: generalized pipeline parallelism for dnn training

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T12:16:11.708368Z

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Observation a4d62a99-e85b-4e0d-86d5-17278d726ffe · outbound

This paper cites Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM

Reference 43

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Observation 171e47ce-a5d5-4175-8455-3b458f4b96e8 · outbound

This paper cites Mitigating harm in language models with conditional-likelihood filtration.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Mitigating harm in language models with conditional-likelihood filtration

Reference 44

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Observation b62bf687-faa3-4741-ba03-c1d63af740f4 · outbound

This paper cites Transformers without Tears: Improving the Normalization of Self-Attention.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Transformers without Tears: Improving the Normalization of Self-Attention

Reference 45

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Adversarial NLI: A New Benchmark for Natural Language Understanding

Reference 46

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Reference 47

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures

Reference 48

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 49

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Reference 50

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Sentiwordnet

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Language models are unsupervised multitask learners

Reference 52

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Unresolved cited work

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 54

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Zero: Memory optimizations toward training trillion parameter models

Reference 55

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning

Reference 56

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Deepspeed: System optimiza- tions enable training deep learning models with over 100 billion parameters

Reference 57

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Winogrande: An adver- sarial winograd schema challenge at scale

Reference 58

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Multitask Prompted Training Enables Zero-Shot Task Generalization

Reference 59

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Reference 60

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 61

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model The woman worked as a babysitter: On biases in language generation

Reference 62

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 63

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Reference 64

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model DeepSpeed

Reference 65

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model A Simple Method for Commonsense Reasoning

Reference 66

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Attention Is All You Need

Reference 67

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective

Reference 68

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Towards Zero-Label Language Learning

Reference 69

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Finetuned Language Models Are Zero-Shot Learners

Reference 70

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Ethical and social risks of harm from Language Models

Reference 71

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Challenges in Detoxifying Language Models

Reference 72

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model A broad-coverage challenge corpus for sen- tence understanding through inference

Reference 73

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning

Reference 74

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Learning and Evaluating General Linguistic Intelligence

Reference 75

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Hellaswag: Can a machine really ﬁnish your sentence? In ACL

Reference 76

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Defending Against Neural Fake News

Reference 77

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Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model PanGu-$\alpha$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation

Reference 78

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Pith citing papers

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PaLM: Scaling Language Modeling with Pathways cites this paper.

PaLM: Scaling Language Modeling with Pathways Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 145

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GPT-NeoX-20B: An Open-Source Autoregressive Language Model cites this paper.

GPT-NeoX-20B: An Open-Source Autoregressive Language Model Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 87

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MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning cites this paper.

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

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OPT: Open Pre-trained Transformer Language Models cites this paper.

OPT: Open Pre-trained Transformer Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 280

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Large Language Models are Zero-Shot Reasoners cites this paper.

Large Language Models are Zero-Shot Reasoners Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

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Efficient Training of Language Models to Fill in the Middle cites this paper.

Efficient Training of Language Models to Fill in the Middle Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

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Atlas: Few-shot Learning with Retrieval Augmented Language Models cites this paper.

Atlas: Few-shot Learning with Retrieval Augmented Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

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Atlas: Few-shot Learning with Retrieval Augmented Language Models cites this paper.

Atlas: Few-shot Learning with Retrieval Augmented Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 249

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FP8 Formats for Deep Learning cites this paper.

FP8 Formats for Deep Learning Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 18

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Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks cites this paper.

Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 28

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Language Models can Solve Computer Tasks cites this paper.

Language Models can Solve Computer Tasks Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 64

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BloombergGPT: A Large Language Model for Finance cites this paper.

BloombergGPT: A Large Language Model for Finance Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 105

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A Survey of Large Language Models cites this paper.

A Survey of Large Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 115

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Observation bf6ab664-31cc-48a9-9a6b-e8cee9a02e2b · inbound

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling cites this paper.

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 113

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Observation 1d2bd2c8-6aed-4f51-814a-19248819316f · inbound

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment cites this paper.

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 112

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MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models cites this paper.

MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 16

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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes cites this paper.

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 98

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StarCoder: may the source be with you! cites this paper.

StarCoder: may the source be with you! Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 96

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Observation 3ed52d22-268b-4b83-91c8-185a940931b4 · inbound

Scaling Data-Constrained Language Models cites this paper.

Scaling Data-Constrained Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 107

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Observation 559e2ffa-4344-4b44-8ed1-33d5930b0bf0 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 117

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DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models cites this paper.

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 85

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MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning cites this paper.

MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 46

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The Falcon Series of Open Language Models cites this paper.

The Falcon Series of Open Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 46

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Observation a7a3a822-d535-4b32-8004-9a7c8f3e918d · inbound

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism cites this paper.

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 164

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Observation ca5a0c81-b324-4ea3-b347-a8a154f6ebfe · inbound

Large Language Models: A Survey cites this paper.

Large Language Models: A Survey Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 101

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Observation 7af723dd-e793-4bde-b21d-2bc5f83e8c60 · inbound

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model cites this paper.

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 162

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Observation 95d34cb4-cc34-4d1c-b590-c1067d1ebeb8 · inbound

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs cites this paper.

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 115

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Observation 68750bdd-ab4f-4e91-b674-5265d232da8f · inbound

SEDD: Scalable and Efficient Dataset Deduplication with GPUs cites this paper.

SEDD: Scalable and Efficient Dataset Deduplication with GPUs Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T06:47:39.580914Z

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Observation 4b54afb2-b5b9-488b-8db0-17fa88158b11 · inbound

MiniMax-01: Scaling Foundation Models with Lightning Attention cites this paper.

MiniMax-01: Scaling Foundation Models with Lightning Attention Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 38

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Observation d2031268-7be2-48ea-80f6-85d7535c0870 · inbound

Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning cites this paper.

Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 247

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Observation 8526b1fe-2dca-4403-8acc-9cf99df5dfa8 · inbound

Language Models Improve When Pretraining Data Matches Target Tasks cites this paper.

Language Models Improve When Pretraining Data Matches Target Tasks Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 96

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Observation 7ab3e7fe-6e51-40fb-9964-594166fb7d91 · inbound

The Carbon Cost of Conversation, Sustainability in the Age of Language Models cites this paper.

The Carbon Cost of Conversation, Sustainability in the Age of Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 64

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Observation 90d1014f-f333-4cb1-9c37-0284d893ebb7 · inbound

TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference cites this paper.

TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

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Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection cites this paper.

Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 16

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Observation 48c4c3d1-1bc5-4c7a-9ab4-81383c8a8a4c · inbound

Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study cites this paper.

Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 22

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Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training cites this paper.

Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 55

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MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS cites this paper.

MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 23

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Observation f7e4b7d6-fc68-497d-a019-8808945a2ab6 · inbound

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector cites this paper.

Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 49

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Observation 92cb1cd5-ba43-4c9d-b888-2ead73b2ced4 · inbound

Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency cites this paper.

Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 14

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veScale-FSDP: Flexible and High-Performance FSDP at Scale cites this paper.

veScale-FSDP: Flexible and High-Performance FSDP at Scale Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 26

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Observation 9e152b6e-7278-46ae-af21-b87a2560c74c · inbound

M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling cites this paper.

M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 36

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9d979ed2-cb97-4d67-a004-6fafd0d4b770 · inbound

Analyzing Reverse Address Translation Overheads in Multi-GPU Scale-Up Pods cites this paper.

Analyzing Reverse Address Translation Overheads in Multi-GPU Scale-Up Pods Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 99

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Observation 5ddb0c30-bb4e-4b90-924d-f362dcb321eb · inbound

SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention cites this paper.

SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 34

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Observation 2e41a25a-1204-4377-b17e-0fd4c0a8fa19 · inbound

TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training cites this paper.

TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 48

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Observation 0b7d393b-f9b7-4505-92e8-b080fc17d68f · inbound

Cross-Layer Energy Analysis of Multimodal Training on Grace Hopper Superchips cites this paper.

Cross-Layer Energy Analysis of Multimodal Training on Grace Hopper Superchips Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8d33e553-0885-4dc1-b5ee-8dcc54f613a9 · inbound

A Scalable Recipe on SuperMUC-NG Phase 2: Efficient Large-Scale Training of Language Models cites this paper.

A Scalable Recipe on SuperMUC-NG Phase 2: Efficient Large-Scale Training of Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 22

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Observation 9c53ad3d-384c-41fe-8417-da812e31e5ce · inbound

Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction cites this paper.

Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 39

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Observation f24b1559-05c5-411b-98ef-1bc7bf1b1f74 · inbound

Phoenix-VL 1.5 Medium Technical Report cites this paper.

Phoenix-VL 1.5 Medium Technical Report Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 21

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Observation e19f136e-48f6-4eb9-8d69-e1d27dc92593 · inbound

Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference cites this paper.

Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 14

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Observation 529ce6db-d9d7-4fbf-a375-380928af73f6 · inbound

Large Language Model Selection with Limited Annotations cites this paper.

Large Language Model Selection with Limited Annotations Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 101

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 16bc8f80-e4bd-46ec-bb85-73c17165dfd3 · inbound

Heterogeneous Parallelism for Multimodal Large Language Model Training cites this paper.

Heterogeneous Parallelism for Multimodal Large Language Model Training Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 15

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f451607f-3301-470c-b5c4-d2f56466ada0 · inbound

Specific Domain Ontology Construction Using Large Language Models cites this paper.

Specific Domain Ontology Construction Using Large Language Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 14

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 41110de5-87f0-4039-b6f1-6564292b6913 · inbound

CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems cites this paper.

CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-10T16:37:23.050768Z

Source-reported events for the cited work

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Observation dae08bfb-ab49-4177-b12c-1d65d6e46998 · inbound

SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text cites this paper.

SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 62

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Unavailable: canonical work link unavailable.

Observation 2504b164-29fb-4361-bbf5-ae4a643ac76f · inbound

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization cites this paper.

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 248

Resolution: unresolved; no resolver link, observed 2026-08-01T06:48:52.175260Z

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Unavailable: canonical work link unavailable.