Infrastructure / Core ML frameworks & libraries

DeepSpeed

DeepSpeed is a deep-learning optimization library for efficient distributed training and inference of very large models. It provides system-level innovations including ZeRO memory optimization, ZeRO-Infinity, 3D parallelism, and mixture-of-experts support, and has powered training of models like BLOOM (176B) and Megatron-Turing NLG (530B). It is maintained by Microsoft and integrates with Hugging Face Transformers, PyTorch Lightning, and others.

Verified live 2026-06-22 via primary sources. Apache-2.0 license body confirmed; full source public.

Openness

5 high confidence

5.0

license: Apache-2.0(OSI)
source: public
core-gated: ungated

Apache-2.0 license body confirmed; full source public.

https://github.com/deepspeedai/DeepSpeed/blob/master/LICENSE recorded 2026-06-22
Apache License Version 2.0 full text

Adoption

3 high confidence

3.0

PyPI last_month downloads ~1.37M.

https://pypistats.org/api/packages/deepspeed/recent recorded 2026-06-22
last_month=1,374,390 downloads

Capability

5 high confidence

5.0

Foundational scale-out training stack used for many of the largest published models.

https://github.com/deepspeedai/DeepSpeed recorded 2026-06-22
README: ZeRO, ZeRO-Infinity, 3D-Parallelism, MoE; powered BLOOM 176B

Unchanged since 2026-07-30 (last edited, not re-checked)