Comparison
gpt-neox vs optimum-tpu
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 optimum-tpu if optimum-tpu is tailored for Python developers working with transformers models aiming to leverage the power of Google TPUs.
Markdown twin · gpt-neox alternatives · optimum-tpu alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | gpt-neox | optimum-tpu |
|---|---|---|
| Maintenance | Steady (56d since push) As of 2w · github_public_v1 | Archived (193d 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 | Published findings 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
- optimum-tpu
- Google TPU optimizations for transformers models
Stars
- gpt-neox
- 7.5k
- optimum-tpu
- 135
Forks
- gpt-neox
- 1.1k
- optimum-tpu
- 30
Open issues
- gpt-neox
- 111
- optimum-tpu
- 4
Language
- gpt-neox
- Python
- optimum-tpu
- 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.
- optimum-tpu
- optimum-tpu is tailored for Python developers working with transformers models aiming to leverage the power of Google TPUs.
Persona
- gpt-neox
- -
- optimum-tpu
- -
Runtime
- gpt-neox
- -
- optimum-tpu
- -
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
- optimum-tpu
- Apache-2.0
Last pushed
- gpt-neox
- Jun 11, 2026
- optimum-tpu
- Jan 23, 2026
Categories
- gpt-neox
- LLM Frameworks, Model Training
- optimum-tpu
- Model Training
Trust and health
Maintenance
- gpt-neox
- Steady (60%)
- optimum-tpu
- Archived (8%)
Days since push
- gpt-neox
- 56d
- optimum-tpu
- 193d
Archived on GitHub
- gpt-neox
- No
- optimum-tpu
- Yes
Open issues (now)
- gpt-neox
- 111
- optimum-tpu
- 4
OSV dependency advisories
- gpt-neox
- No lockfile (source not queried)
- optimum-tpu
- Published findings
Full report
- gpt-neox
- Trust report
- optimum-tpu
- 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.
- 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 optimum-tpu if…
- Tags unique to optimum-tpu: optimizations, tpu.
- Use optimum-tpu when you require high performance execution of transformers models on Google TPUs, as it offers specific optimizations for that hardware.
- Leaner open-issue backlog (4).
When NOT to use optimum-tpu
- Avoid using optimum-tpu if your infrastructure does not include or will not support Google TPUs, since its optimizations are not beneficial on other hardware.
- Skip this tool if you are working in environments with strict licensing requirements as it requires adherence to the Apache-2.0 license.
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 (huggingface/optimum-tpu) · observed Aug 4, 2026
- GitHub forks (huggingface/optimum-tpu) · observed Aug 4, 2026
- Last push (huggingface/optimum-tpu) · observed Jan 23, 2026
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gpt-neox 7.5k · optimum-tpu 135 (synced Aug 7, 2026).
Common questions
- What is the difference between gpt-neox and optimum-tpu?
- gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. optimum-tpu: Google TPU optimizations for transformers models. See the comparison table for live GitHub stats and shared categories.
- When should I choose gpt-neox over optimum-tpu?
- Choose gpt-neox over optimum-tpu 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; 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 optimum-tpu over gpt-neox?
- Choose optimum-tpu over gpt-neox when Tags unique to optimum-tpu: optimizations, tpu; Use optimum-tpu when you require high performance execution of transformers models on Google TPUs, as it offers specific optimizations for that hardware; Leaner open-issue backlog (4).
- 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 optimum-tpu?
- Avoid using optimum-tpu if your infrastructure does not include or will not support Google TPUs, since its optimizations are not beneficial on other hardware. Skip this tool if you are working in environments with strict licensing requirements as it requires adherence to the Apache-2.0 license.
- Is gpt-neox or optimum-tpu more popular on GitHub?
- gpt-neox has more GitHub stars (7,452 vs 135). Stars measure visibility, not whether either tool fits your constraints.
- Are gpt-neox and optimum-tpu open source?
- Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, optimum-tpu: Apache-2.0).
- Where can I find alternatives to gpt-neox or optimum-tpu?
- GraphCanon lists graph-backed alternatives at gpt-neox alternatives and optimum-tpu alternatives (gpt-neox markdown twin, optimum-tpu 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 optimum-tpu?
- gpt-neox: Steady. optimum-tpu: 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 optimum-tpu?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; optimum-tpu trust report.