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Jeff Rasley

Jeff Rasley has 3 papers in our library, in 2021–2024, most of them on Language models & pretraining, Text generation, GPU & parallel computing; often with Samyam Rajbhandari, Ammar Ahmad Awan, Reza Yazdani Aminabadi.

3 papers in our library · 2021–2024

OpenAlex · ORCID · h-index 15 · 75 works · 3,796 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1,798 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3944

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢Snowflake Inc.Bozeman · United States2026 · 1 papercurrent🎓Butler UniversityIndpls · United States2002–2014 · 21 papers🎓Brown UniversityProvidence · United States2014–2017 · 6 papers🏢MicrosoftRedmond · United States2019–2022 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 3 worksHardware and Architecture · 3 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.GPU & parallel computing · 1 workLanguage models & pretraining · 1 workText generation · 1 work

 Works by year

2020s

20241 paper20221 paper20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale (2022 · 261 citations)
    Mixture-of-Experts (MoE) models can reach a given quality with far less training compute than dense networks.
  2. ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning (2021 · 254 citations)
    Dense model sizes have grown roughly a thousandfold in three years, while single-GPU memory has grown only about fivefold.
  3. DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference (2024 · 78 citations)
    LLM serving systems must deliver high throughput and low latency together, which is hard when prompts are long.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model ↗arXiv:2211.05100 · no summary yetBigScience Workshop et al.2022 · 1,606 citations
  2. DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI ScalearXiv:2201.05596Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al.2022 · 261 citations
  3. ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep LearningSC 2021Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, et al.2021 · 254 citations
  4. DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-InferencearXiv 2024 (Microsoft DeepSpeed)Connor Holmes, Masahiro Tanaka, Michael Wyatt, et al.2024 · 78 citations

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 1,798 times (of the 149,439 papers whose bibliographies we read); 390 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jeff-rasley/