deepspeedai/DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

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[](https://github.com/deepspeedai/DeepSpeed/blob/master/LICENSE) [](https://pypi.org/project/deepspeed/) [](https://pepy.tech/project/deepspeed) [](#build-pipeline-status) [](https://www.bestpractices.dev/projects/9530) [](https://twitter.com/intent/follow?screenname=DeepSpeedAI) [](https://twitter.com/DeepSpeedAIJP) [](https://www.zhihu.com/people/deepspeed) [](https://join.slack.com/t/deepspeedworkspace/sharedinvite/zt-3a8pjd8dd-PCj2hMvR4Y2syPwVnjEoww) English · 简体中文 DeepSpeed hosts regular office hours on the last Tuesday of each month at 12:00 America/NewYork to discuss development plans, features, etc. This meeting is public for anyone to join and ask questions. The meeting is hosted on Zoom and can be joined here. More news [2025/04] DeepCompile: Unlocking Compiler Optimization for Distributed Training [2025/03] DeepSpeed AutoTP: Automatic Tensor Parallel Training of Hugging Face models [2024/12] Ulysses-Offload: Democratizing Long Context LLM Training DeepSpeed enabled the world's most powerful language models (at the time of this writing) such as MT-530B and BLOOM. DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations include ZeRO, ZeRO-Infinity, 3D-Parallelism, Ulysses Sequence Parallelism, DeepSpeed-MoE, etc. DeepSpeed was…

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What this repository does

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Repository facts

Owner: deepspeedai
Primary language: Python
Stars: 43,279
Forks: 5,060
Open issues + pull requests: 1,589
License: Apache-2.0
Archived: No
Default branch: master
Created: 2020-01-23T18:35:18.000Z
Last push: 2026-10-31T10:57:40.000Z

Topics and intended use

Owner-supplied topics: billion-parameters, compression, data-parallelism, deep-learning, gpu, inference, machine-learning, mixture-of-experts, model-parallelism, pipeline-parallelism, pytorch, trillion-parameters, zero

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