Enables single machine or distributed training and evaluation of deep learning models.
Open source
24 alternativesDeepSpeed
DeepSpeed optimizes distributed training and inference for deep learning models.
About DeepSpeed
DeepSpeed is an open-source deep learning optimization library for distributed training and inference. It includes ZeRO, ZeRO-Infinity, 3D-parallelism, Ulysses sequence parallelism, and DeepSpeed-MoE, and integrates with frameworks including Transformers, Accelerate, Lightning, MosaicML, Determined, and MMEngine.
It has been used to train large-scale models such as Megatron-Turing NLG (530B) and BLOOM (176B). The project holds public office hours on the last Tuesday of each month at 12:00 America/New_York.
Key features
- Supports distributed training of deep learning models
- Supports distributed inference of deep learning models
- Includes ZeRO and ZeRO-Infinity memory optimization
- Includes 3D-parallelism for large-scale training
- Includes Ulysses sequence parallelism
- Integrates with Transformers, Accelerate, Lightning, MosaicML, Determined, MMEngine
Best for: Developers and researchers training large deep learning models with distributed computing resources.
Good for
- Open-source library for distributed training and inference
- Integrates with multiple open-source deep learning frameworks
- Used to train large-scale models such as MT-530B and BLOOM
DeepSpeed at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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DeepSpeed FAQ
When are DeepSpeed office hours?
On the last Tuesday of each month at 12:00 America/New_York, hosted on Zoom and open to the public.
Which frameworks are listed as integrated with DeepSpeed?
Transformers, Accelerate, Lightning, MosaicML, Determined, and MMEngine.
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