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DeepSpeed
What DeepSpeed is
DeepSpeed is a deep learning optimization library for distributed model training and inference, aimed at researchers and practitioners handling large-scale workloads. It supports mixed precision, data, model and pipeline parallelism, and the ZeRO optimizer, which partitions model states and gradients across data-parallel processes to reduce memory use. For transformer-based PyTorch models, DeepSpeed-Inference offers model parallelism, inference-customized kernels and model quantization. The project describes its API as a lightweight PyTorch wrapper for distributed training, mixed precision, gradient accumulation and checkpoints. Listed integrations include Hugging Face Transformers, Accelerate, PyTorch Lightning and MosaicML; it also states compatibility with Megatron and combining data and 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 metrics to TensorBoard, WandB or CSV files. It supports Python, GPU acceleration and local training, with listed support for Linux, macOS and self-hosting. It is Apache-2.0 licensed and free.
Who it suits
DeepSpeed suits deep learning researchers and practitioners working on large-scale training or inference. It is relevant to teams using PyTorch, distributed training, or the listed framework integrations.
What it does well
- Supports mixed precision and several forms of parallelism.
- ZeRO partitions model states and gradients to reduce memory use.
- Inference supports model quantization for transformer-based PyTorch models.
- Monitor logs metrics to TensorBoard, WandB or CSV.
- Free and Apache-2.0 licensed.
What to know first
- Listed platforms are Linux, macOS and self-hosted.
- Megatron and inference features are framed around specific model workflows.
Limits
The Data Efficiency Library's reported savings are for specified workloads. The stated inference support is for transformer-based PyTorch models.
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| DeepSpeed | Open-source software library · Apache-2.0 license | Free |
As deepspeed.ai lists them, read 4 Oct 2026. The maker's own page has the final word.
Getting started
- Visit the DeepSpeed website.
- Choose a supported environment: Linux, macOS or self-hosted.
- Use the getting-started guide to select among the named CPU and accelerator options.
- Use the Python library with PyTorch and, where relevant, a listed integration.
Verdict
DeepSpeed combines distributed training and inference tools with integrations and monitoring options. It is free, with support and features geared toward large-scale deep learning work.
Questions
How much does DeepSpeed cost?
DeepSpeed is free. Its listed plan is free.
Is DeepSpeed open source?
Yes. The project is identified as open source under the Apache-2.0 license.
Which platforms does DeepSpeed support?
The listed platforms are Linux, macOS and self-hosted. The getting-started guide names several CPU and accelerator options.
What does DeepSpeed support for training?
Training features include mixed precision, data, model and pipeline parallelism, and the ZeRO optimizer.
What integrations are listed?
The site lists Hugging Face Transformers, Accelerate, PyTorch Lightning and MosaicML. DeepSpeed also states compatibility with Megatron.
Does DeepSpeed offer a free trial?
No. The listed free-trial status is no; the library has a free plan.
Facts from DeepSpeed's own pages
- 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. · deepspeed.ai, 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. · deepspeed.ai, 4 Oct 2026
- Inference
- DeepSpeed-Inference supports model parallelism, inference-customized kernels and model quantization for transformer-based PyTorch models. · deepspeed.ai, 4 Oct 2026
- Integrations
- The site lists integrations with Hugging Face Transformers, Accelerate, PyTorch Lightning and MosaicML. · deepspeed.ai, 4 Oct 2026
- Intended users
- The project describes its audience as deep learning researchers and practitioners working on large-scale training and inference. · microsoft.com, 4 Oct 2026
- License
- The GitHub repository identifies DeepSpeed as an open-source project under the Apache-2.0 license. · github.com, 4 Oct 2026
- Megatron compatibility
- DeepSpeed states that it is fully compatible with Megatron and supports combining its data parallelism with model parallelism. · deepspeed.ai, 4 Oct 2026
- Monitoring
- The DeepSpeed Monitor can log live training metrics to TensorBoard, WandB or CSV files. · deepspeed.ai, 4 Oct 2026
- Purpose
- DeepSpeed is a deep learning optimization library for distributed model training and inference. · github.com, 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. · deepspeed.ai, 4 Oct 2026
- Security
- The repository links to a SECURITY file and identifies the project as Apache-2.0 licensed. · github.com, 4 Oct 2026
- Support
- The GitHub repository says DeepSpeed holds public office hours on the last Tuesday of each month. · github.com, 4 Oct 2026
- Training
- Its training features include mixed precision, data, model and pipeline parallelism, and the ZeRO optimizer. · deepspeed.ai, 4 Oct 2026
- ZeRO memory optimization
- ZeRO partitions model states and gradients across data-parallel processes to reduce memory use. · deepspeed.ai, 4 Oct 2026
Where DeepSpeed stands on Freedom251
- Deep Learning Software#8 of 35