Mohammad Shoeybi Google Scholar: h-index, Total Citations, and Citation Map
Mohammad Shoeybi's h-index is 47 (91 i10-index, 20,308+ total citations across 135+ publications) according to Google Scholar as of November 2026. Mohammad Shoeybi is affiliated with NVIDIA.
Mohammad Shoeybi is a researcher affiliated with NVIDIA, specializing in Large Language Models, NLP, Multi-Modal Models. Their work has been cited 20,308 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Mohammad Shoeybi's Citation Metrics
Bibliometric impact based on 135 indexed publications.
- H-Index
- 47
- i10-Index
- 91
- Total Citations
- 20,308
- Citing Countries
- 32
As of November 2026.
Mohammad Shoeybi has an h-index of 47 and 20,308 total citations across 135 publications, with research cited by institutions in 32 countries.
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Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
20194,100
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
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Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Mohammad Shoeybi's research
Mohammad Shoeybi is a researcher in Large Language Models, NLP and Multi-Modal Models at NVIDIA. Their work has been cited 20,308 times across 135 publications (h-index 47), according to Google Scholar.
Their most-cited work, “Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism” (2019), has accumulated 4,100 citations. Other influential works include “Bloom: A 176b-parameter open-access multilingual language model” (2022) with 2,589 citations and “Efficient large-scale language model training on gpu clusters using megatron-lm” (2021) with 1,912 citations.
Citations of Mohammad Shoeybi's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
