DeepSpeed

Mac · Linux · Self-hosted

Freedom report

Two barsScore 6.4

Free tierA free tier is on its own pricing page
Open codeNo open-source code on record
Runs widely2 of 6 device platforms
DocumentedPlans, terms and facts published

DeepSpeed is ranked #9 of 36 in deep learning software on Freedom251. It runs on Linux, macOS, Self-hosted. There is a free plan.

DeepSpeed plans and pricing

All plans

DeepSpeed Free Open-source software library · Apache-2.0 license github.com · 4 Oct 2026

Compared on deep learning software

Free plan: Yes
Training mode: local
Deployment targets: multiple
GPU acceleration: Yes
Distributed training: Yes
Supported languages: Python

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The record

Purpose: DeepSpeed is a deep learning optimization library for distributed model training and inference.
Training: Its training features include mixed precision, data, model and pipeline parallelism, and the ZeRO optimizer.
Inference: DeepSpeed-Inference supports model parallelism, inference-customized kernels and model quantization for transformer-based PyTorch models.
PyTorch API: DeepSpeed describes its API as a lightweight wrapper around PyTorch that manages distributed training, mixed precision, gradient accumulation and checkpoints.
Integrations: The site lists integrations with Hugging Face Transformers, Accelerate, PyTorch Lightning and MosaicML.
Megatron compatibility: DeepSpeed states that it is fully compatible with Megatron and supports combining its data parallelism with model parallelism.
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.
ZeRO memory optimization: ZeRO partitions model states and gradients across data-parallel processes to reduce memory use.
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.
Monitoring: The DeepSpeed Monitor can log live training metrics to TensorBoard, WandB or CSV files.
Intended users: The project describes its audience as deep learning researchers and practitioners working on large-scale training and inference.
Support: The GitHub repository says DeepSpeed holds public office hours on the last Tuesday of each month.
Security: The repository links to a SECURITY file and identifies the project as Apache-2.0 licensed.
License: The GitHub repository identifies DeepSpeed as an open-source project under the Apache-2.0 license.
Price from: Free

Where it ranks on Freedom251

Best Deep Learning Software in 2026#9 of 36

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Sources

github.com/deepspeedai/DeepSpeed· checked 4 Oct 2026
deepspeed.ai/training/· checked 4 Oct 2026
deepspeed.ai/inference/· checked 4 Oct 2026
deepspeed.ai· checked 4 Oct 2026
deepspeed.ai/getting-started/· checked 4 Oct 2026
microsoft.com/en-us/research/project/deepspeed/· checked 4 Oct 2026