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Comparison

gpt-neox vs GLM-130B

Verdict

Pick gpt-neox if gPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an Apache-2.0 license; pick GLM-130B if gLM-130B specializes in bilingual capabilities and is open source under the Apache-2.0 license.

Markdown twin · gpt-neox alternatives · GLM-130B alternatives

GraphCanon updated 2w

gpt-neox logo

gpt-neox

EleutherAI/gpt-neox

★ 7.5kpushed Jun 11, 2026
vs
GLM-130B logo

GLM-130B

zai-org/GLM-130B

★ 7.7kpushed Jul 25, 2023

Trust & integrity

Signalgpt-neoxGLM-130B
Maintenance
Steady (56d since push)
As of 2w · github_public_v1
Dormant (1103d since push)
As of 3w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 3w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No published findings from this source as of 2026-07-11
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
gpt-neox trust reportGLM-130B trust reportTrust methodology

Tagline

gpt-neox
Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries
GLM-130B
GLM-130B: An Open Bilingual Pre-Trained Model

Stars

gpt-neox
7.5k
GLM-130B
7.7k

Forks

gpt-neox
1.1k
GLM-130B
600

Open issues

gpt-neox
111
GLM-130B
124

Language

gpt-neox
Python
GLM-130B
Python

Adopt for

gpt-neox
GPT-NeoX from EleutherAI leverages GPU-based model parallelism via Megatron and DeepSpeed libraries to facilitate the training of large-scale autoregressive transformers in Python, under an Apache-2.0 license.
GLM-130B
GLM-130B specializes in bilingual capabilities and is open source under the Apache-2.0 license.

Persona

gpt-neox
-
GLM-130B
-

Runtime

gpt-neox
-
GLM-130B
-

License

gpt-neox
The tool is licensed under Apache-2.0, allowing permissive use but emphasizing that derivative works must preserve copyright headers and licenses as per their origins
GLM-130B
The GLM-130B codebase and framework are available under the permissive Apache-2.0 license; however, usage of model weights is governed by its own Model License.

Last pushed

gpt-neox
Jun 11, 2026
GLM-130B
Jul 25, 2023

Categories

gpt-neox
LLM Frameworks, Model Training
GLM-130B
LLM Frameworks, Model Training

Trust and health

Maintenance

gpt-neox
Steady (60%)
GLM-130B
Dormant (18%)

Days since push

gpt-neox
56d
GLM-130B
1103d

Open issues (now)

gpt-neox
111
GLM-130B
124

OSV dependency advisories

gpt-neox
No lockfile (source not queried)
GLM-130B
No published findings from this source as of 2026-07-11

Full report

gpt-neox
Trust report
GLM-130B
Trust report

Choose gpt-neox if…

  • Pricing: Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations..
  • Tags unique to gpt-neox: deepspeed-library, gpt-3, transformers.
  • - When your project requires a framework based on state-of-the-art libraries like Megatron and DeepSpeed that are optimized for large GPU clusters.

When NOT to use gpt-neox

  • - In scenarios where minimal hardware resources, such as a single low-memory GPU or CPU-only environments, are available for training due to GPT-NeoX's requirement for a large-scale infrastructure.
  • - If your project is limited by the Apache License terms or requires proprietary codebases without open-source contributions and modifications from external parties.
GitHubDetails

Choose GLM-130B if…

  • Pricing: Free to use with specific licensing requirements for model weights..
  • Requirements: Min 8 GB RAM.
  • Tags unique to GLM-130B: bilingual, iclr 2023, pre-trained.
  • Use GLM-130B when you need strong support for two languages to facilitate multilingual content creation or processing, given its specialized training in bilingual contexts.

When NOT to use GLM-130B

  • Avoid GLM-130B if your application demands single-language proficiency exclusively, as its strength lies specifically in bilingual support.
  • Do not use this model if you seek a resource with multilingual capabilities beyond two specific languages, since it focuses particularly on only a dual-language environment.
GitHubDetails

Explore

gpt-neox trust report →GLM-130B trust report →LLM Frameworks category →Model Training 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 7.5k · GLM-130B 7.7k (synced Aug 7, 2026).

Common questions

What is the difference between gpt-neox and GLM-130B?
gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. GLM-130B: GLM-130B: An Open Bilingual Pre-Trained Model. See the comparison table for live GitHub stats and shared categories.
When should I choose gpt-neox over GLM-130B?
Choose gpt-neox over GLM-130B when Pricing: Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations.; Tags unique to gpt-neox: deepspeed-library, gpt-3, transformers; - When your project requires a framework based on state-of-the-art libraries like Megatron and DeepSpeed that are optimized for large GPU clusters.
When should I choose GLM-130B over gpt-neox?
Choose GLM-130B over gpt-neox when Pricing: Free to use with specific licensing requirements for model weights.; Requirements: Min 8 GB RAM; Tags unique to GLM-130B: bilingual, iclr 2023, pre-trained; Use GLM-130B when you need strong support for two languages to facilitate multilingual content creation or processing, given its specialized training in bilingual contexts.
When should I avoid gpt-neox?
- In scenarios where minimal hardware resources, such as a single low-memory GPU or CPU-only environments, are available for training due to GPT-NeoX's requirement for a large-scale infrastructure. - If your project is limited by the Apache License terms or requires proprietary codebases without open-source contributions and modifications from external parties.
When should I avoid GLM-130B?
Avoid GLM-130B if your application demands single-language proficiency exclusively, as its strength lies specifically in bilingual support. Do not use this model if you seek a resource with multilingual capabilities beyond two specific languages, since it focuses particularly on only a dual-language environment.
Is gpt-neox or GLM-130B more popular on GitHub?
GLM-130B has more GitHub stars (7,656 vs 7,452). Stars measure visibility, not whether either tool fits your constraints.
Are gpt-neox and GLM-130B open source?
Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, GLM-130B: Apache-2.0).
Where can I find alternatives to gpt-neox or GLM-130B?
GraphCanon lists graph-backed alternatives at gpt-neox alternatives and GLM-130B alternatives (gpt-neox markdown twin, GLM-130B 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 GLM-130B?
gpt-neox: Steady. GLM-130B: Dormant. 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 GLM-130B?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; GLM-130B trust report.

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