What it does
Core capabilities at a glance
- Billion Parameters
- Compression
- Data Parallelism
- GPU
- Inference
- Mixture OF Experts
- Model Parallelism
- Pipeline Parallelism
Deep dive
The full breakdown - performance, comparisons, and setup
DeepSpeed
DeepSpeed is a fine-tuning toolkit - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Overview
DeepSpeed hosts regular office hours on the last Tuesday of each month at 12:00 America/New_York to discuss development plans, features, etc. This meeting is public for anyone to join and ask questions. The meeting is hosted on Zoom and can be joined here.
- [2025/10] We hosted the Ray x DeepSpeed Meetup at Anyscale. We shared our most recent work on SuperOffload, ZenFlow, Muon Optimizer Support, Arctic Long Sequence Training and DeepCompile. Please find the meetup slides here.
DeepSpeed enabled the world's most powerful language models (at the time of this writing) such as MT-530B and BLOOM. 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 landscape in terms of scale that is possible. These innovations include ZeRO, ZeRO-Infinity, 3D-Parallelism, Ulysses Sequence Parallelism, DeepSpeed-MoE, etc.
DeepSpeed was an important part of Microsoft’s AI at Scale initiative to enable next-generation AI capabilities at scale, where you can find more information here.
DeepSpeed is open-source, written primarily in Python, with 43,218 GitHub stars under the Apache 2.0 license. The latest release is v0.19.7 (2026-09-16).
Key capabilities
From the project's documentation:
- 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.
- NVIDIA: Pascal, Volta, Ampere, and Hopper architectures
- AMD: MI100 and MI200
- DeepSpeed now support various HW accelerators.
- Registration is free and all videos are available on-demand.
How it fits a local-AI stack
DeepSpeed runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related fine-tuning toolkits in the directory:
Sources
- Source code & docs: deepspeedai/DeepSpeed
- Official website: https://www.deepspeed.ai/
Stats from GitHub, 2026-10-10.
Frequently asked
Quick answers to common questions
What is DeepSpeed?
DeepSpeed is a fine-tuning tool for local AI workloads. DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Is DeepSpeed free and open source?
Yes, DeepSpeed has 43,218 GitHub stars and is licensed under Apache 2.0. You can self-host it for free on .
What hardware do I need for DeepSpeed?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. DeepSpeed has 43,218 GitHub stars and an active community.
Does DeepSpeed support GPU acceleration?
DeepSpeed's GPU support depends on your specific setup. Check the documentation for details. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to DeepSpeed?
Popular alternatives include other fine-tuning tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does DeepSpeed cost?
DeepSpeed is free-open-source. It is completely free and open source to self-host.
Pairs well with
Complementary tools, models, and hardware