--- title: "gpt-neox vs Megatron-LM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-nvidia-megatron-lm" tools: ["eleutherai-gpt-neox", "nvidia-megatron-lm"] --- # gpt-neox vs Megatron-LM *GraphCanon updated Sep 20, 2026* ## Verdict Pick gpt-neox if gPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries; pick Megatron-LM if megatron-LM is a research project by NVIDIA for training large transformer models at scale, emphasizing parallelism strategies across multiple GPUs. [gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 118 open issues, last pushed Sep 4, 2026. [Megatron-LM](https://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html) has 18k stars, 4.5k forks, and 1.4k open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [Megatron-LM's repository](https://github.com/NVIDIA/Megatron-LM). | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [Megatron-LM](/tools/nvidia-megatron-lm.md) | | --- | --- | --- | | Tagline | Model parallel autoregressive transformers on GPUs | Ongoing research training transformer models at scale | | Stars | 7,461 | 17,938 | | Forks | 1,121 | 4,530 | | Open issues | 118 | 1,368 | | Language | Python | Python | | Adopt for | GPT-NeoX is a model parallel autoregressive transformer implementation on GPUs, leveraging Megatron and DeepSpeed libraries. | Megatron-LM is a research project by NVIDIA for training large transformer models at scale, emphasizing parallelism strategies across multiple GPUs. | | 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. | The license for Megatron-LM is categorized as 'Other', indicating that it may have specific terms and conditions that differ from standard open-source licenses. | | Categories | LLM Frameworks, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [Megatron-LM](/tools/nvidia-megatron-lm.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 13d | 0d | | Open issues (now) | 118 | 1.4k | | Stars delta | +9 (30d) | +597 (30d) | | Open issues delta | +7 (30d) | +256 (30d) | | Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/nvidia-megatron-lm/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: Megatron-LM - **Requirements:** Building from source can be memory-intensive, and users may need to limit parallel compilation jobs by setting MAX_JOBS to manage memory usage. - **Adopt for:** Megatron-LM is a research project by NVIDIA for training large transformer models at scale, emphasizing parallelism strategies across multiple GPUs. - **License detail:** The license for Megatron-LM is categorized as 'Other', indicating that it may have specific terms and conditions that differ from standard open-source licenses. ## Choose when ### Choose gpt-neox if… - License: gpt-neox is Apache-2.0, Megatron-LM is Other. - 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, language-model. - Also covers LLM Frameworks. - When you need to train large language models on GPUs and require model parallelism to handle the computational demands. ### Choose Megatron-LM if… - License: Megatron-LM is Other, gpt-neox is Apache-2.0. - Requirements: Building from source can be memory-intensive, and users may need to limit parallel compilation jobs by setting MAX_JOBS to manage memory usage.. - Tags unique to Megatron-LM: large-language-models, model-para, nvidia, parallelism. - Use Megatron-LM when you need to train large transformer models and have access to multiple GPUs, as it excels in parallelism strategies like tensor parallelism (TP), pipeline parallelism (PP), data-2 ## 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 Megatron-LM - Avoid using Megatron-LM if your project does not require large-scale model training or if you do not have access to multiple GPUs, as its benefits are most pronounced in these scenarios. - Do not use Megatron-LM if your team lacks the expertise to handle complex parallelism strategies or if your infrastructure cannot support the memory and computational demands of building from source. ## Common questions ### What is the difference between gpt-neox and Megatron-LM? gpt-neox: Model parallel autoregressive transformers on GPUs. Megatron-LM: Ongoing research training transformer models at scale. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpt-neox over Megatron-LM? Choose gpt-neox over Megatron-LM when License: gpt-neox is Apache-2.0, Megatron-LM is Other; 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, language-model; 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 Megatron-LM over gpt-neox? Choose Megatron-LM over gpt-neox when License: Megatron-LM is Other, gpt-neox is Apache-2.0; Requirements: Building from source can be memory-intensive, and users may need to limit parallel compilation jobs by setting MAX_JOBS to manage memory usage.; Tags unique to Megatron-LM: large-language-models, model-para, nvidia, parallelism; Use Megatron-LM when you need to train large transformer models and have access to multiple GPUs, as it excels in parallelism strategies like tensor parallelism (TP), pipeline parallelism (PP), data-2. ### 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 Megatron-LM? Avoid using Megatron-LM if your project does not require large-scale model training or if you do not have access to multiple GPUs, as its benefits are most pronounced in these scenarios. Do not use Megatron-LM if your team lacks the expertise to handle complex parallelism strategies or if your infrastructure cannot support the memory and computational demands of building from source. ### Is gpt-neox or Megatron-LM more popular on GitHub? Megatron-LM has more GitHub stars (17,938 vs 7,461). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt-neox and Megatron-LM open source? Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, Megatron-LM: Other). ### Where can I find alternatives to gpt-neox or Megatron-LM? GraphCanon lists graph-backed alternatives at [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) and [Megatron-LM alternatives](/tools/nvidia-megatron-lm/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md), [Megatron-LM markdown twin](/tools/nvidia-megatron-lm/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-nvidia-megatron-lm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpt-neox or Megatron-LM? gpt-neox: Active. Megatron-LM: 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 Megatron-LM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust); [Megatron-LM trust report](/tools/nvidia-megatron-lm/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/_