Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
2019 • 3365 Citations
Published in arXiv preprint (arXiv:1909.08053)
Mohammad Shoeybi
Mohammad Shoeybi is a researcher affiliated with Senior Director of Applied Research at NVIDIA, specializing in AI & Machine Learning. Their work has been cited 16,800 times. This profile visualizes their global influence, highlighting strong citation networks in CN.
AI & Machine Learning
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0i10-index
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Senior Director of Applied Research at NVIDIA•
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Mohammad Shoeybi's Citation Metrics
Bibliometric impact based on 1 indexed publication.
- H-Index
- 1
- i10-Index
- 1
- Total Citations
- 16,800
- Citing Countries
- 4
As of April 2026.
Mohammad Shoeybi has an h-index of 1 and 16,800 total citations across 1 publication, with research cited by institutions in 4 countries.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
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Top Citing Countries(Limited)
China(CN)
7Bangladesh(BD)
6Hong Kong(HK)
2Australia(AU)
1Top Citing Institutions(Limited)
ByteDance
7United International University
5The University of Hong Kong
2Bangladesh University of Engineering and Technology (BUET)
1Charles Darwin University
1Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Citer Influence Network
Visa-ready visualisation: each outer ring node is a country citing this scholar. Node size & edge weight scale with citing-paper count. Showing top 4 of 4 countries (16 total citations).
Letter of Support — Candidate Recommenders
Ranked by EB-1A / O-1A relevance: independence from your home institution, affiliation with a top-ranked institution, repeat citations of your work, and recency. Reach out to the highest-scoring candidates to request support letters.
- #1
Guangming Sheng
8.5The University of Hong Kong
Hong Kong· Cited your work 1 time
IndependentTop institutionDiversifies geography - #2
Chuan Wu
8.5The University of Hong Kong
Hong Kong· Cited your work 1 time
IndependentTop institutionDiversifies geography - #3
Ru Zhang
5.0ByteDance
China· Cited your work 1 time
Independent - #4
Chi Zhang
5.0ByteDance
China· Cited your work 1 time
Independent - #5
Kaniz Fatema
6.5United International University
Bangladesh· Cited your work 1 time
IndependentDiversifies geography - #6
Nur Mohammad Fahad
6.5United International University
Bangladesh· Cited your work 1 time
IndependentDiversifies geography - #7
Sami Azam
6.5Charles Darwin University
Australia· Cited your work 1 time
IndependentDiversifies geography - #8
Zilingfeng Ye
5.0ByteDance
China· Cited your work 1 time
Independent - #9
Xibin Wu
5.0ByteDance
China· Cited your work 1 time
Independent - #10
Wang Zhang
5.0ByteDance
China· Cited your work 1 time
Independent
Scores combine 5 signals: independence (+3), prestige institution (+2), repeat citations (+2 per paper, cap 5), recent activity (+1), and geography diversity (+1.5). Identity matching uses name + institution; namesakes at the same org are merged.
Cited by 5 papers
5 / 5 independent
Citations flagged as non-independent share the scholar's home institution (NVIDIA). EB-1A & O-1A petitions typically quote the independent-citation count as the stronger evidence of outside recognition.
· cites “Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism”
Kaniz Fatema, Nur Mohammad Fahad, Sadman Sakib, Most Marufatul Jannat Mim + 5 more
IEEE Access· cites “Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism”
· cites “Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism”
Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu + 5 more
Proceedings of the Twentieth European Conference on Computer Systems (EuroSys '25)· cites “Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism”
· cites “Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism”
Top Cited Works(Showing 1 of 1)
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Citation Trend (Last 10 Years)
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