--- title: "gpt-neox vs Liger-Kernel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-linkedin-liger-kernel" tools: ["eleutherai-gpt-neox", "linkedin-liger-kernel"] --- # gpt-neox vs Liger-Kernel *GraphCanon updated Aug 7, 2026* ## 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 Liger-Kernel if optimized Triton kernels for accelerating LLM training, especially on ROCm PyTorch installations. [gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 111 open issues, last pushed Jun 11, 2026. [Liger-Kernel](https://linkedin.github.io/Liger-Kernel/) has 6.6k stars, 573 forks, and 190 open issues, last pushed Aug 7, 2026. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [Liger-Kernel's repository](https://github.com/linkedin/Liger-Kernel). | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [Liger-Kernel](/tools/linkedin-liger-kernel.md) | | --- | --- | --- | | Tagline | Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries | Efficient Triton Kernels for LLM Training | | Stars | 7,452 | 6,555 | | Forks | 1,119 | 573 | | Open issues | 111 | 190 | | Language | Python | Python | | Adopt for | 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. | Optimized Triton kernels for accelerating LLM training, especially on ROCm PyTorch installations. | | Persona | - | - | | Runtime | - | - | | License | 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 | BSD-2-Clause | | 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) | [Liger-Kernel](/tools/linkedin-liger-kernel.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 56d | 0d | | Open issues (now) | 111 | 190 | | Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/linkedin-liger-kernel/trust.md) | ## Decision facts: gpt-neox - **Pricing:** freemium - Free to use with the caveat of adhering to the Apache License terms, particularly in preserving copyright and license headers for all derivations. - **Adopt for:** 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. - **License detail:** 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 ## Decision facts: Liger-Kernel - **Adopt for:** Optimized Triton kernels for accelerating LLM training, especially on ROCm PyTorch installations. ## Choose when ### Choose gpt-neox if… - License: gpt-neox is Apache-2.0, Liger-Kernel is BSD-2-Clause. - 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, transformers. - 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. ### Choose Liger-Kernel if… - License: Liger-Kernel is BSD-2-Clause, gpt-neox is Apache-2.0. - Tags unique to Liger-Kernel: finetuning, gemma2, llama, mistral. - When enhancing training speed of large language models with ROCm-compatible hardware. ## 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. ## When NOT to use Liger-Kernel - Avoid if only CUDA environments are supported, as Liger-Kernel emphasizes ROCm compatibility. - Skip for simple setup requirements; prefer more streamlined tools without extensive customization options. ## Common questions ### What is the difference between gpt-neox and Liger-Kernel? gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. Liger-Kernel: Efficient Triton Kernels for LLM Training. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpt-neox over Liger-Kernel? Choose gpt-neox over Liger-Kernel when License: gpt-neox is Apache-2.0, Liger-Kernel is BSD-2-Clause; 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, transformers; 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 Liger-Kernel over gpt-neox? Choose Liger-Kernel over gpt-neox when License: Liger-Kernel is BSD-2-Clause, gpt-neox is Apache-2.0; Tags unique to Liger-Kernel: finetuning, gemma2, llama, mistral; When enhancing training speed of large language models with ROCm-compatible hardware. ### 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 Liger-Kernel? Avoid if only CUDA environments are supported, as Liger-Kernel emphasizes ROCm compatibility. Skip for simple setup requirements; prefer more streamlined tools without extensive customization options. ### Is gpt-neox or Liger-Kernel more popular on GitHub? gpt-neox has more GitHub stars (7,452 vs 6,555). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt-neox and Liger-Kernel open source? Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, Liger-Kernel: BSD-2-Clause). ### Where can I find alternatives to gpt-neox or Liger-Kernel? GraphCanon lists graph-backed alternatives at [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) and [Liger-Kernel alternatives](/tools/linkedin-liger-kernel/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md), [Liger-Kernel markdown twin](/tools/linkedin-liger-kernel/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-linkedin-liger-kernel.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpt-neox or Liger-Kernel? gpt-neox: Steady. Liger-Kernel: 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 Liger-Kernel? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust); [Liger-Kernel trust report](/tools/linkedin-liger-kernel/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/_