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DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale paper by Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, A. A. Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He
⭐ 61.1
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Paper

DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale

by Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, A. A. Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He 2207.00032
Free2AITools Nexus Index
61.1
S: Semantic 50

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A: Authority 91
P: Popularity 70
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

The landscape of transformer model inference is increasingly diverse in model size, model characteristics, latency and throughput requirements, hardware requirements, etc. With such diversity, designing a versatile inference system is challenging. DeepSpeed-Inference addresses these challenges by (1) a multi-GPU inference solution to minimize latency while maximizing throughput for both dense and sparse transformers when the model fits in aggregate GPU memory, and (2) a heterogeneous inferenc...

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Semantic Scholar 518 Citations
Paper Information Summary
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Registry ID 2207.00032
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2207_00032,
  author = {Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, A. A. Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He},
  title = {DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2207.00032}},
  note = {Accessed via Free2AITools.}
}
APA Style
Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, A. A. Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He. (2026). DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale [Paper]. Free2AITools. https://arxiv.org/abs/2207.00032

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Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 91
Popularity (P) 70
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale: Authority (A:91), Popularity (P:70), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
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πŸ“ Executive Summary

"The landscape of transformer model inference is increasingly diverse in model size, model characteristics, latency and throughput requirements, hardware requirements, etc. With such diversity, designing a versatile inference system is challenging. DeepSpeed-Inference addresses these challenges by (1) a multi-GPU inference solution to minimize latency while maximizing throughput for both dense and sparse transformers when the model fits in aggregate GPU memory, and (2) a heterogeneous inferenc..."

❝ Cite Node 

@article{Aminabadi2026DeepSpeed-,
  title={DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale},
  author={Reza Yazdani Aminabadi and Samyam Rajbhandari and Minjia Zhang and A. A. Awan and Cheng Li and Du Li and Elton Zheng and Jeff Rasley and Shaden Smith and Olatunji Ruwase and Yuxiong He},
  journal={arXiv preprint arXiv:2207.00032},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Reza Yazdani Aminabadi Samyam Rajbhandari Minjia Zhang A. A. Awan Cheng Li Du Li Elton Zheng Jeff Rasley Shaden Smith Olatunji Ruwase Yuxiong He

πŸ”— Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

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πŸ“Š Research Signals

πŸ“ˆ518CitationsSemantic Scholar
πŸ›οΈ91AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈinfrastructure opsField

🏷️ Research Topics

transformer architecture
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πŸ†” Identity & Source

id
2207.00032
slug
2207.00032
source
semantic_scholar
author
Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, A. A. Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

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null
params billions
null
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null
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