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DeepSpeed
frameworkbillion-parameters
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Overview
Developed byMicrosoft
Founded2020
LicenseApache 2.0
Open source✓ Open Source
Use casedeep learning training optimization
Knowledge graph stats
Claims42
Avg confidence90%
Avg freshness100%
Last updatedUpdated 19 days ago
Trust distribution
100% unverified
Governance
EU Riskminimal risk
Training data disclosure

DeepSpeed

framework

deepspeedai/DeepSpeed is an open source AI framework. DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. Built with Python, 42,178 GitHub stars.

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primary use case

ValueTrustConfidenceFreshnessSources
deep learning training optimization○UnverifiedHighFresh1
deep learning optimization and model training acceleration○UnverifiedHighFresh1
deep learning model training optimization○UnverifiedHighFresh1
distributed deep learning training optimization○UnverifiedHighFresh1
Deep learning model training optimization and memory efficiency○UnverifiedHighFresh1
deep learning optimization and inference acceleration○UnverifiedHighFresh1
deep learning optimization and training acceleration○UnverifiedHighFresh1
deep learning optimization and distributed training○UnverifiedHighFresh1
deep learning optimization library○UnverifiedHighFresh1

license type

ValueTrustConfidenceFreshnessSources
Apache 2.0○UnverifiedHighFresh1
MIT License○UnverifiedHighFresh1
Apache License 2.0○UnverifiedHighFresh1

open source

ValueTrustConfidenceFreshnessSources
true○UnverifiedHighFresh1

maintained by

ValueTrustConfidenceFreshnessSources
Microsoft○UnverifiedHighFresh1

developed by

ValueTrustConfidenceFreshnessSources
Microsoft○UnverifiedHighFresh1

supports protocol

ValueTrustConfidenceFreshnessSources
ZeRO○UnverifiedHighFresh1
distributed training○UnverifiedHighFresh1
ZeRO optimizer states partitioning○UnverifiedHighFresh1
ZeRO optimizer○UnverifiedHighFresh1
CUDA○UnverifiedModerateFresh1
MPI○UnverifiedModerateFresh1
NCCL○UnverifiedModerateFresh1

integrates with

ValueTrustConfidenceFreshnessSources
PyTorch○UnverifiedHighFresh1
Hugging Face Transformers○UnverifiedHighFresh1

requires

ValueTrustConfidenceFreshnessSources
PyTorch○UnverifiedHighFresh1
Python○UnverifiedHighFresh1
CUDA○UnverifiedModerateFresh1

pricing model

ValueTrustConfidenceFreshnessSources
free○UnverifiedHighFresh1

supports feature

ValueTrustConfidenceFreshnessSources
ZeRO optimizer○UnverifiedHighFresh1
model parallelism○UnverifiedHighFresh1
gradient compression○UnverifiedModerateFresh1

written in

ValueTrustConfidenceFreshnessSources
Python○UnverifiedHighFresh1

supports model

ValueTrustConfidenceFreshnessSources
transformer models○UnverifiedModerateFresh1

founded year

ValueTrustConfidenceFreshnessSources
2020○UnverifiedModerateFresh1

first released year

ValueTrustConfidenceFreshnessSources
2020○UnverifiedModerateFresh1

alternative to

ValueTrustConfidenceFreshnessSources
FasterTransformer○UnverifiedModerateFresh1
Horovod○UnverifiedModerateFresh1
FairScale○UnverifiedModerateFresh1

competes with

ValueTrustConfidenceFreshnessSources
Horovod○UnverifiedModerateFresh1
TensorRT○UnverifiedModerateFresh1
NVIDIA Apex○UnverifiedModerateFresh1
FairScale○UnverifiedModerateFresh1

Alternatives & Similar Tools

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Horovod
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TensorRT
competes with
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NVIDIA Apex
competes with
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Horovod
alternative to
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FairScale
alternative to
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FairScale
competes with
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Commonly Used With

PyTorchHugging Face Transformers

Related entities

Graph Insights

5 entities depend on DeepSpeed
Pipeline ParallelismDeepSpeed-MIIGPU Memory OptimizationTensor ParallelismModel Parallelism
View full impact analysis
Top sources (42 claims traced)
competes_withhighsource
primary_use_casehighsource
primary_use_casehighsource
supports_protocolhighsource
primary_use_casehighsource
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Claim count: 42Last updated: 4/23/2026Edit history