DeepSpeed
Deep learning optimization library for efficient distributed training and inference
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Decision brief
Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression.
Good fit when
- - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters)
- - For efficient distributed training requiring specific optimizations like ZeRO, which significantly reduces memory usage for massive models
Avoid when
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs
- - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
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Install
pip install DeepSpeedSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
DeepSpeed is a Python-based deep learning library aimed at facilitating efficient distributed training and inference, supporting PyTorch with optimizations like compression, data parallelism, model parallelism, and pipeline parallelism.
Capability facts
- Languages
- python
Source: github.language · Aug 7, 2026
Categories
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README
Installation
The quickest way to get started with DeepSpeed is via pip, this will install the latest release of DeepSpeed which is not tied to specific PyTorch or CUDA versions. 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++ extension loader that relies on ninja to build and dynamically link them at runtime.
Requirements
- PyTorch must be installed before installing DeepSpeed.
- For full feature support we recommend a version of PyTorch that is >= 2.0 and ideally the latest PyTorch stable release.
- A CUDA or ROCm compiler such as nvcc or hipcc used to compile C++/CUDA/HIP extensions.
- Specific GPUs we develop and test against are listed below, this doesn't mean your GPU will not work if it doesn't fall into this category it's just DeepSpeed is most well tested on the following:
- NVIDIA: Pascal, Volta, Ampere, and Hopper architectures
- AMD: MI100 and MI200
For agents
This page has a .md twin and JSON over the API.