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Home/Compare/gpt-neox vs ColossalAI

Comparison

gpt-neox vs ColossalAI

Verdict

Pick gpt-neox when tags unique to gpt-neox: gpt-3, python, deepspeed-library, transformers; pick ColossalAI when tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training.

Markdown twin · gpt-neox alternatives · ColossalAI alternatives

GraphCanon updated today

gpt-neox logo

gpt-neox

EleutherAI/gpt-neox

★ 7.4kpushed Jun 11, 2026
vs
ColossalAI logo

ColossalAI

hpcaitech/ColossalAI

★ 41kpushed May 25, 2026

Trust & integrity

Signalgpt-neoxColossalAI
Maintenance
Active (29d since push)
As of today · github_public_v1
Steady (46d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No lockfile
As of today · none
gpt-neox trust reportColossalAI trust reportTrust methodology

Tagline

gpt-neox
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
ColossalAI
Making large AI models cheaper, faster and more accessible

Stars

gpt-neox
7.4k
ColossalAI
41k

Forks

gpt-neox
1.1k
ColossalAI
4.5k

Open issues

gpt-neox
104
ColossalAI
501

Language

gpt-neox
Python
ColossalAI
Python

Adopt for

gpt-neox
-
ColossalAI
ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models.

Persona

gpt-neox
-
ColossalAI
-

Runtime

gpt-neox
-
ColossalAI
-

License

gpt-neox
Apache-2.0
ColossalAI
Apache-2.0

Last pushed

gpt-neox
Jun 11, 2026
ColossalAI
May 25, 2026

Categories

gpt-neox
Model Training
ColossalAI
Model Training, Inference & Serving

Trust and health

Maintenance

gpt-neox
Active (82%)
ColossalAI
Steady (60%)

Days since push

gpt-neox
29d
ColossalAI
46d

Open issues (now)

gpt-neox
104
ColossalAI
501

Full report

gpt-neox
Trust report
ColossalAI
Trust report

Choose gpt-neox if…

  • Tags unique to gpt-neox: gpt-3, python, deepspeed-library, transformers.
  • More recently updated (last pushed Jun 11, 2026).

When NOT to use gpt-neox

  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
GitHubDetails

Choose ColossalAI if…

  • Tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training.
  • Also covers Inference & Serving.
  • You require handling extremely large AI models with massive context windows, such as over 2M tokens.

When NOT to use ColossalAI

  • You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems.
  • Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series).
  • You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7.
GitHubDetails

Explore

gpt-neox trust report →ColossalAI trust report →Model Training category →Inference & Serving category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: gpt-neox 7.4k · ColossalAI 41k (synced Jul 11, 2026).

Common questions

What is the difference between gpt-neox and ColossalAI?
gpt-neox: An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries. ColossalAI: Making large AI models cheaper, faster and more accessible. See the comparison table for live GitHub stats and shared categories.
When should I choose gpt-neox over ColossalAI?
Choose gpt-neox over ColossalAI when Tags unique to gpt-neox: gpt-3, python, deepspeed-library, transformers; More recently updated (last pushed Jun 11, 2026).
When should I choose ColossalAI over gpt-neox?
Choose ColossalAI over gpt-neox when Tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens.
When should I avoid gpt-neox?
Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
When should I avoid ColossalAI?
You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7.
Is gpt-neox or ColossalAI more popular on GitHub?
ColossalAI has more GitHub stars (41,408 vs 7,443). Stars measure visibility, not whether either tool fits your constraints.
Are gpt-neox and ColossalAI open source?
Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, ColossalAI: Apache-2.0).
Where can I find alternatives to gpt-neox or ColossalAI?
GraphCanon lists graph-backed alternatives at gpt-neox alternatives and ColossalAI alternatives (gpt-neox markdown twin, ColossalAI markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, gpt-neox or ColossalAI?
gpt-neox: Active. ColossalAI: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for gpt-neox and ColossalAI?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; ColossalAI trust report.