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DeepSpeed

DeepSpeed is an open-source library for optimizing distributed deep-learning training and inference.

Place
#19 of 35
Score
6.0
DeepSpeed's website

What DeepSpeed is

DeepSpeed is an open-source library for optimizing distributed deep-learning training and inference. Its training features include mixed precision, data, model and pipeline parallelism, plus the ZeRO optimizer, which partitions model states and gradients across data-parallel processes to reduce memory use. For transformer-based PyTorch models, DeepSpeed-Inference supports model parallelism, inference-focused kernels and quantization. The project describes its API as a lightweight PyTorch wrapper that manages distributed training, mixed precision, gradient accumulation and checkpoints. Listed integrations include Hugging Face Transformers, Accelerate, PyTorch Lightning and MosaicML; DeepSpeed also states compatibility with Megatron and combining data parallelism with model parallelism. Its Data Efficiency Library uses curriculum learning and random layerwise token dropping, with up to 2x data and time savings reported for specified workloads. DeepSpeed Monitor can log live training metrics to TensorBoard, WandB or CSV files. The getting-started guide names support for AMD ROCm, Intel Xeon CPU, Intel Data Center Max Series XPU, Intel Gaudi HPU and Huawei Ascend NPU. The library is free under the Apache-2.0 license.

Good

  • Free Apache-2.0 open-source library
  • Supports data, model and pipeline parallelism
  • ZeRO partitions model states and gradients
  • Integrates with listed PyTorch ecosystem tools
  • Monitor can log metrics to TensorBoard, WandB or CSV

Watch out

  • No free trial is listed
  • Data and time savings are reported only for specified workloads

Who it suits

DeepSpeed is aimed at deep-learning researchers and practitioners working on large-scale training and inference. It may suit Python users seeking distributed training, model parallelism or inference optimization.

Verdict

DeepSpeed offers training and inference optimization tools for distributed deep-learning workflows. Its feature set is centered on large-scale use, so users should check its integrations and accelerator support against their setup.

Plans and prices

From DeepSpeed's own pricing page, checked 4 Oct 2026.

All about pricing
PlanPriceWhat it gives you
DeepSpeedFreeOpen-source software library · Apache-2.0 license

The facts

Each one read from DeepSpeed's own pages, with the date it was checked.

What it does

Accelerators
The getting-started guide names AMD ROCm, Intel Xeon CPU, Intel Data Center Max Series XPU, Intel Gaudi HPU and Huawei Ascend NPU support.checked 4 Oct 2026
Data efficiency
The Data Efficiency Library uses curriculum learning and random layerwise token dropping, with the site reporting up to 2x data and time savings for specified workloads.checked 4 Oct 2026
Inference
DeepSpeed-Inference supports model parallelism, inference-customized kernels and model quantization for transformer-based PyTorch models.checked 4 Oct 2026
Integrations
The site lists integrations with Hugging Face Transformers, Accelerate, PyTorch Lightning and MosaicML.checked 4 Oct 2026
Intended users
The project describes its audience as deep learning researchers and practitioners working on large-scale training and inference.checked 4 Oct 2026
License
The GitHub repository identifies DeepSpeed as an open-source project under the Apache-2.0 license.checked 4 Oct 2026
Megatron compatibility
DeepSpeed states that it is fully compatible with Megatron and supports combining its data parallelism with model parallelism.checked 4 Oct 2026
Monitoring
The DeepSpeed Monitor can log live training metrics to TensorBoard, WandB or CSV files.checked 4 Oct 2026
Purpose
DeepSpeed is a deep learning optimization library for distributed model training and inference.checked 4 Oct 2026
PyTorch API
DeepSpeed describes its API as a lightweight wrapper around PyTorch that manages distributed training, mixed precision, gradient accumulation and checkpoints.checked 4 Oct 2026
Security
The repository links to a SECURITY file and identifies the project as Apache-2.0 licensed.checked 4 Oct 2026
Support
The GitHub repository says DeepSpeed holds public office hours on the last Tuesday of each month.checked 4 Oct 2026
Training
Its training features include mixed precision, data, model and pipeline parallelism, and the ZeRO optimizer.checked 4 Oct 2026
ZeRO memory optimization
ZeRO partitions model states and gradients across data-parallel processes to reduce memory use.checked 4 Oct 2026

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