Comparison
gpt-neox vs mesh
Verdict
Pick gpt-neox if gPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries; 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 Sep 20, 2026
12views this month
Trust & integrity
| Signal | gpt-neox | mesh |
|---|---|---|
| Maintenance | Active (13d since push) As of Sep 18, 2026 · github_public_v1 | Archived (1023d since push) As of Sep 6, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 6, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- Model parallel autoregressive transformers on GPUs
- 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
- 118
- mesh
- 98
Language
- gpt-neox
- Python
- mesh
- Python
Adopt for
- gpt-neox
- GPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries.
- 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
- GPT-NeoX is licensed under the Apache License, Version 2.0, which allows for free use, modification, and distribution of the software, provided that the license terms are adhered to.
- mesh
- Apache-2.0
Last pushed
- gpt-neox
- Sep 4, 2026
- mesh
- Nov 17, 2023
Categories
- gpt-neox
- LLM Frameworks, Model Training
- mesh
- Model Training
Trust and health
Maintenance
- gpt-neox
- Active (82%)
- mesh
- Archived (8%)
Days since push
- gpt-neox
- 13d
- mesh
- 1023d
Archived on GitHub
- gpt-neox
- No
- mesh
- Yes
Open issues (now)
- gpt-neox
- 118
- mesh
- 98
Stars delta
- gpt-neox
- +9 (30d)
- mesh
- 0 (30d)
Open issues delta
- gpt-neox
- +7 (30d)
- mesh
- 0 (30d)
Full report
- gpt-neox
- Trust report
- mesh
- Trust report
Choose gpt-neox if…
- Requirements: Min 16 GB RAM; Requires GPU support for optimal performance.; Integration with Megatron and DeepSpeed libraries is essential for full functionality..
- Tags unique to gpt-neox: autoregressive-transformers, deepspeed-library, gpt-3, gpu.
- Also covers LLM Frameworks.
- When you need to train large language models on GPUs and require model parallelism to handle the computational demands.
When NOT to use gpt-neox
- If your project does not require the scale of model parallelism and can be handled with single-GPU setups, GPT-NeoX might introduce unnecessary complexity.
- When the specific features of Megatron and DeepSpeed are not aligned with your project's needs, opting for a simpler framework might be more suitable.
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 Sep 20, 2026
- GitHub forks (EleutherAI/gpt-neox) · observed Sep 20, 2026
- Last push (EleutherAI/gpt-neox) · observed Sep 4, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (tensorflow/mesh) · observed Sep 20, 2026
- GitHub forks (tensorflow/mesh) · observed Sep 20, 2026
- Last push (tensorflow/mesh) · observed Nov 17, 2023
- License file (Apache-2.0) · observed Sep 20, 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 Sep 20, 2026).
Common questions
- What is the difference between gpt-neox and mesh?
- gpt-neox: Model parallel autoregressive transformers on GPUs. 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 Requirements: Min 16 GB RAM; Requires GPU support for optimal performance.; Integration with Megatron and DeepSpeed libraries is essential for full functionality.; Tags unique to gpt-neox: autoregressive-transformers, deepspeed-library, gpt-3, gpu; Also covers LLM Frameworks; When you need to train large language models on GPUs and require model parallelism to handle the computational demands.
- 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?
- If your project does not require the scale of model parallelism and can be handled with single-GPU setups, GPT-NeoX might introduce unnecessary complexity. When the specific features of Megatron and DeepSpeed are not aligned with your project's needs, opting for a simpler framework might be more suitable.
- 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 (7,461 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: Active. 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.