Comparison
gpt-neox vs mesh
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 mesh if mesh TensorFlow supports simplified model parallelism across multiple devices in Python under the Apache-2.0 license.
Markdown twin · gpt-neox alternatives · mesh alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | gpt-neox | mesh |
|---|---|---|
| Maintenance | Steady (56d since push) As of 2w · github_public_v1 | Archived (993d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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 |
Tagline
- gpt-neox
- Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries
- mesh
- Mesh TensorFlow: Model Parallelism Made Easier
Stars
- gpt-neox
- 7.5k
- mesh
- 1.6k
Forks
- gpt-neox
- 1.1k
- mesh
- 255
Open issues
- gpt-neox
- 111
- mesh
- 98
Language
- gpt-neox
- Python
- mesh
- 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.
- mesh
- Mesh TensorFlow supports simplified model parallelism across multiple devices in Python under the Apache-2.0 license.
Persona
- gpt-neox
- -
- mesh
- -
Runtime
- gpt-neox
- -
- mesh
- -
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
- mesh
- Apache-2.0
Last pushed
- gpt-neox
- Jun 11, 2026
- mesh
- Nov 17, 2023
Categories
- gpt-neox
- LLM Frameworks, Model Training
- mesh
- Model Training
Trust and health
Maintenance
- gpt-neox
- Steady (60%)
- mesh
- Archived (8%)
Days since push
- gpt-neox
- 56d
- mesh
- 993d
Archived on GitHub
- gpt-neox
- No
- mesh
- Yes
Open issues (now)
- gpt-neox
- 111
- mesh
- 98
Full report
- gpt-neox
- Trust report
- mesh
- 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, language-model, transformers.
- Also covers LLM Frameworks.
- - 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.
Choose mesh if…
- Tags unique to mesh: model parallelism, python, tensorflow.
- When working on large models that benefit from being split across many devices.
- Leaner open-issue backlog (98).
When NOT to use mesh
- If you are looking for a tool that simplifies other aspects of machine learning beyond model-parallel computation.
- For projects with limited GPU/TPU resources where multi-device parallelism is not required.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (EleutherAI/gpt-neox) · observed Aug 7, 2026
- GitHub forks (EleutherAI/gpt-neox) · observed Aug 7, 2026
- Last push (EleutherAI/gpt-neox) · observed Jun 11, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorflow/mesh) · observed Aug 7, 2026
- GitHub forks (tensorflow/mesh) · observed Aug 7, 2026
- Last push (tensorflow/mesh) · observed Nov 17, 2023
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gpt-neox 7.5k · mesh 1.6k (synced Aug 7, 2026).
Common questions
- What is the difference between gpt-neox and mesh?
- gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. mesh: Mesh TensorFlow: Model Parallelism Made Easier. See the comparison table for live GitHub stats and shared categories.
- When should I choose gpt-neox over mesh?
- Choose gpt-neox over mesh 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, language-model, transformers; Also covers LLM Frameworks; - 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 mesh over gpt-neox?
- Choose mesh over gpt-neox when Tags unique to mesh: model parallelism, python, tensorflow; When working on large models that benefit from being split across many devices; Leaner open-issue backlog (98).
- 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 mesh?
- If you are looking for a tool that simplifies other aspects of machine learning beyond model-parallel computation. For projects with limited GPU/TPU resources where multi-device parallelism is not required.
- Is gpt-neox or mesh more popular on GitHub?
- gpt-neox has more GitHub stars (7,452 vs 1,630). Stars measure visibility, not whether either tool fits your constraints.
- Are gpt-neox and mesh open source?
- Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, mesh: Apache-2.0).
- Where can I find alternatives to gpt-neox or mesh?
- GraphCanon lists graph-backed alternatives at gpt-neox alternatives and mesh alternatives (gpt-neox markdown twin, mesh 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 mesh?
- gpt-neox: Steady. mesh: Archived. 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 mesh?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; mesh trust report.