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deepspeedaiOrganization

deepspeedai

@deepspeedai • Open source projects from deepspeedai. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.

Portfolio concentration

96%

Top three share

Shows whether the organization is driven by one breakout repo or several visible projects.

Breadth

6 repos

Visible snapshot

2 repositories updated in the last 90 days.

Leading language

Python

Portfolio mix

Python (5), C++ (1)

Average size

8.9K

Stars per repository

Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.

Back to organizationsCompare repositories
Updated: 2025-03-26(399d ago)GitHub API fallback6 repositories

Portfolio Shape

96%

of the visible star count comes from this organization's top three repositories.

Average Repository Size

8.9K

stars per repository in this same snapshot.

Current Mix

Python

is the most common language here, with 2 repositories updated in the last 90 days.

Why this rank

This organization stands out because one flagship repo drives 79% of its visible star count.

Flagship share 79%Breakout repo: DeepSpeed

Organization pages work best when you separate portfolio breadth from flagship concentration. In deepspeedai's case, the visible top three repositories account for about 96% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.

The dominant language mix here is Python (5), C++ (1). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.

Source: GitHub API fallback. This is the same cache-first snapshot used by the organization ranking list, so the summary view and the detail view should stay aligned.

Top Repositories

#RepositoryLanguage⭐ Stars
1deepspeedai/DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Python42.2K
2deepspeedai/DeepSpeedExamples

Example models using DeepSpeed

Python6.8K
3deepspeedai/Megatron-DeepSpeed

Ongoing research training transformer language models at scale, including: BERT & GPT-2

Python2.2K
4deepspeedai/DeepSpeed-MII

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.

Python2.1K
5deepspeedai/DeepSpeed-KernelsC++72
6deepspeedai/deepspeed-gpt-neox

An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.

Python21

Next step after the organization read

Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
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Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
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How to Read This Snapshot

Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.

For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.