DeepSpeed
https://github.com/microsoft/DeepSpeed
Microsoft's distributed-training library — ZeRO optimizer states/grads/params partitioning; ZeRO-Inference for serving. Foundation under many OSS LLM training stacks.
At a glance
- Type
- ZeRO-based distributed training
- Tier
- T1
- Section
- Training infrastructure
- Created
- 2020
- Latest release
- searched not found
- License
- Apache-2.0
- GitHub
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- Pricing
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- Funding
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Taxonomy
- storage
- n/a
- retrieval
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- persistence
- n/a
- update
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- unit
- n/a
- governance
- n/a
- conflict
- n/a
When to use
Optimised for: searched not found
Anti-fit: searched not found
Pros & cons
Pros
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Cons
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Claims & capabilities
~36k★; widely used with HF/Accelerate.
Technical surface
- API surface
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- Backend storage
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- Deployment
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- Embedding model
- not applicable — not a memory product
- Multi-tenancy
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- MCP
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- A2A
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- OpenTelemetry
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Compare DeepSpeed with…
Similar systems
Other training infrastructure in the catalog, ranked by inbound references.
- Azure Machine Learning T1
Microsoft Azure's managed ML platform — pipelines, AutoML, Designer, prompt flow (LLM). Heavy integration with Azure OpenAI and Azure AI Studio.
- LoRA T3
Microsoft Research's Low-Rank Adapters method — trains rank-r matrices added to attention weights; 10000x parameter reduction. Foundation of all adapter-style fine-tuning.
- DPO T3
Stanford method that converts RLHF into a supervised-learning loss over preference pairs — no separate reward model needed; widely adopted alignment recipe.
- GRPO T3
DeepSeek-Math's GRPO — group-relative advantage estimation replacing PPO's critic. Used in DeepSeek-R1 reasoning post-training; widely adopted in OSS reasoning RLHF.
- Accelerate (Hugging Face) T1
Hugging Face's abstraction over distributed-training backends (DDP, FSDP, DeepSpeed, Megatron). Minimal code change to scale a PyTorch script.
- Argilla T2
Open-source data-quality platform for LLM training data — human-in-the-loop labelling, RLHF data collection, dataset curation. Acquired by Hugging Face 2024.