deepspeedai/DeepSpeed

Project overview

DeepSpeed is a PyTorch-integrated library that applies system innovations such as ZeRO, ZeRO-Infinity, 3D-Parallelism, and Ulysses Sequence Parallelism to make large-scale deep learning training and inference more efficient and effective.

Project type

Model Development · Data Processing · Infrastructure

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• Developers, researchers, and AI engineers who build and scale large deep learning models.
• Technical users comfortable installing prerequisites and working within a code library environment.

Key capabilities

• Provides system innovations like ZeRO, ZeRO-Infinity, 3D-Parallelism, and Ulysses Sequence Parallelism to make large-scale deep learning training effective and efficient.
• Includes C++/CUDA extensions (ops) that are built just-in-time (JIT) at runtime using torch's JIT C++ extension loader.
• Supports many features for training and inference on Windows.
• Provides a tool to validate installation and check machine compatibility with extensions/ops.

Limitations and risks

• Some features like async io (AIO) and GDS are not supported on Windows.
• Building C++/CUDA extensions just-in-time (JIT) requires a compatible compiler and might fail if the environment is not correctly set up.

Getting started

• Setup difficulty is rated medium. Users must install PyTorch before installing DeepSpeed via pip.
• Users should ensure their environment includes a compatible compiler, as installation involves the JIT compilation of C++/CUDA extensions.

Evidence and sources

• GitHub project description: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
• README: 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 la…
• README: DeepSpeed has been used to train many different large-scale models, below is a list of several examples that we are aware of
• README: DeepSpeed includes several C++/CUDA extensions that we commonly refer to as our 'ops'. By default, all of these extensions/ops will be built just-in-time (JIT) using torch's JIT C…
• README: Many DeepSpeed features are supported on Windows for both training and inference. You can read more about this in the original blog post here. Among features that are currently no…

Reviewed at: 2026-07-16T12:13:01Z