Comparison
gpt-neox vs pytorch-lightning
Verdict
Pick gpt-neox if gPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries; pick pytorch-lightning if pyTorch Lightning scales PyTorch models across GPUs with minimal code changes.
Markdown twin · gpt-neox alternatives · pytorch-lightning alternatives
GraphCanon updated Sep 27, 2026
7views this month
vs
Trust & integrity
| Signal | gpt-neox | pytorch-lightning |
|---|---|---|
| Maintenance | Active (13d since push) As of Sep 18, 2026 · github_public_v1 | Very active (5d since push) As of Sep 27, 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 27, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No published findings from this source as of 2026-07-11 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
- pytorch-lightning
- Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Stars
- gpt-neox
- 7.5k
- pytorch-lightning
- 31k
Forks
- gpt-neox
- 1.1k
- pytorch-lightning
- 3.8k
Open issues
- gpt-neox
- 118
- pytorch-lightning
- 1.1k
Language
- gpt-neox
- Python
- pytorch-lightning
- Python
Adopt for
- gpt-neox
- GPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries.
- pytorch-lightning
- PyTorch Lightning scales PyTorch models across GPUs with minimal code changes.
Persona
- gpt-neox
- -
- pytorch-lightning
- -
Runtime
- gpt-neox
- -
- pytorch-lightning
- -
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.
- pytorch-lightning
- Apache-2.0
Last pushed
- gpt-neox
- Sep 4, 2026
- pytorch-lightning
- Sep 21, 2026
Categories
- gpt-neox
- LLM Frameworks, Model Training
- pytorch-lightning
- Inference & Serving, Model Training
Trust and health
Maintenance
- gpt-neox
- Active (82%)
- pytorch-lightning
- Very active (96%)
Days since push
- gpt-neox
- 13d
- pytorch-lightning
- 5d
Open issues (now)
- gpt-neox
- 118
- pytorch-lightning
- 1.1k
Stars delta
- gpt-neox
- +9 (30d)
- pytorch-lightning
- +96 (30d)
Open issues delta
- gpt-neox
- +7 (30d)
- pytorch-lightning
- +30 (30d)
OSV dependency advisories
- gpt-neox
- No lockfile (source not queried)
- pytorch-lightning
- No published findings from this source as of 2026-07-11
Full report
- gpt-neox
- Trust report
- pytorch-lightning
- 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 pytorch-lightning if…
- Tags unique to pytorch-lightning: ai, artificial-intelligence, data-science, deep-learning.
- Also covers Inference & Serving.
- Scalable ML model training with consistent API across single to multiple GPUs
When NOT to use pytorch-lightning
- For lightweight models requiring minimal configuration or manual control over model distribution
- Projects that target environments without access to multi-GPU setups and do not require scalability features
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 (Lightning-AI/pytorch-lightning) · observed Sep 27, 2026
- GitHub forks (Lightning-AI/pytorch-lightning) · observed Sep 27, 2026
- Last push (Lightning-AI/pytorch-lightning) · observed Sep 21, 2026
- License file (Apache-2.0) · observed Sep 27, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: gpt-neox 7.5k · pytorch-lightning 31k (synced Sep 20, 2026).
Common questions
- What is the difference between gpt-neox and pytorch-lightning?
- gpt-neox: Model parallel autoregressive transformers on GPUs. pytorch-lightning: Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.. See the comparison table for live GitHub stats and shared categories.
- When should I choose gpt-neox over pytorch-lightning?
- Choose gpt-neox over pytorch-lightning 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 pytorch-lightning over gpt-neox?
- Choose pytorch-lightning over gpt-neox when Tags unique to pytorch-lightning: ai, artificial-intelligence, data-science, deep-learning; Also covers Inference & Serving; Scalable ML model training with consistent API across single to multiple GPUs.
- 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 pytorch-lightning?
- For lightweight models requiring minimal configuration or manual control over model distribution Projects that target environments without access to multi-GPU setups and do not require scalability features
- Is gpt-neox or pytorch-lightning more popular on GitHub?
- pytorch-lightning has more GitHub stars (31,363 vs 7,461). Stars measure visibility, not whether either tool fits your constraints.
- Are gpt-neox and pytorch-lightning open source?
- Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, pytorch-lightning: Apache-2.0).
- Where can I find alternatives to gpt-neox or pytorch-lightning?
- GraphCanon lists graph-backed alternatives at gpt-neox alternatives and pytorch-lightning alternatives (gpt-neox markdown twin, pytorch-lightning 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 pytorch-lightning?
- gpt-neox: Active. pytorch-lightning: Very active. 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 pytorch-lightning?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-neox trust report; pytorch-lightning trust report.