--- title: "gpt-neox vs pytorch-lightning" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-lightning-ai-pytorch-lightning" tools: ["eleutherai-gpt-neox", "lightning-ai-pytorch-lightning"] --- # gpt-neox vs pytorch-lightning *GraphCanon updated Sep 27, 2026* ## 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. [gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 118 open issues, last pushed Sep 4, 2026. [pytorch-lightning](https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) has 31k stars, 3.8k forks, and 1.1k open issues, last pushed Sep 21, 2026. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [pytorch-lightning's repository](https://github.com/Lightning-AI/pytorch-lightning). | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) | | --- | --- | --- | | Tagline | Model parallel autoregressive transformers on GPUs | Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes. | | Stars | 7,461 | 31,363 | | Forks | 1,121 | 3,801 | | Open issues | 118 | 1,090 | | Language | Python | Python | | Adopt for | GPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries. | PyTorch Lightning scales PyTorch models across GPUs with minimal code changes. | | Persona | - | - | | Runtime | - | - | | License | 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. | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 13d | 5d | | Open issues (now) | 118 | 1.1k | | Stars delta | +9 (30d) | +96 (30d) | | Open issues delta | +7 (30d) | +30 (30d) | | Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/lightning-ai-pytorch-lightning/trust.md) | ## Decision facts: gpt-neox - **Requirements:** Min 16 GB RAM; Requires GPU support for optimal performance.; Integration with Megatron and DeepSpeed libraries is essential for full functionality. - **Adopt for:** GPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries. - **License detail:** 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. ## Decision facts: pytorch-lightning - **Adopt for:** PyTorch Lightning scales PyTorch models across GPUs with minimal code changes. ## Choose when ### 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. ### 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 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 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 ## 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](/tools/eleutherai-gpt-neox/alternatives) and [pytorch-lightning alternatives](/tools/lightning-ai-pytorch-lightning/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md), [pytorch-lightning markdown twin](/tools/lightning-ai-pytorch-lightning/alternatives.md)), 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](/compare/eleutherai-gpt-neox-vs-lightning-ai-pytorch-lightning.md) 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](/tools/eleutherai-gpt-neox/trust); [pytorch-lightning trust report](/tools/lightning-ai-pytorch-lightning/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eleutherai-gpt-neox`](/api/graphcanon/graph?tool=eleutherai-gpt-neox) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_