--- title: "gpt-neox vs P-tuning-v2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-gpt-neox-vs-thudm-p-tuning-v2" tools: ["eleutherai-gpt-neox", "thudm-p-tuning-v2"] --- # gpt-neox vs P-tuning-v2 *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 P-tuning-v2 if p-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted. [gpt-neox](https://www.eleuther.ai/) reports 7.5k GitHub stars, 1.1k forks, and 111 open issues, last pushed Jun 11, 2026. [P-tuning-v2](https://github.com/THUDM/P-tuning-v2) has 2.1k stars, 213 forks, and 35 open issues, last pushed Nov 16, 2023. Figures are from public GitHub metadata via [gpt-neox's repository](https://github.com/EleutherAI/gpt-neox) and [P-tuning-v2's repository](https://github.com/THUDM/P-tuning-v2). | | [gpt-neox](/tools/eleutherai-gpt-neox.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | | --- | --- | --- | | Tagline | Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries | Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks | | Stars | 7,452 | 2,077 | | Forks | 1,119 | 213 | | Open issues | 111 | 35 | | 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. | P-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning. | | 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 | P-tuning-v2 is provided under the Apache-2.0 license which permits free use, modification and distribution as long as copyright and license notice are preserved. | | 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) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 56d | 990d | | Open issues (now) | 111 | 35 | | Full report | [trust report](/tools/eleutherai-gpt-neox/trust.md) | [trust report](/tools/thudm-p-tuning-v2/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: P-tuning-v2 - **Requirements:** Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance. - **Adopt for:** P-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning. - **License detail:** P-tuning-v2 is provided under the Apache-2.0 license which permits free use, modification and distribution as long as copyright and license notice are preserved. ## Choose when ### 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, 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 P-tuning-v2 if… - Requirements: Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance.. - Tags unique to P-tuning-v2: natural-language-processing, p-tuning, parameter-efficient-learning, pretrained-language-model. - For scenarios requiring efficient use of resources, as P-tuning v2 requires fewer parameters than traditional fine-tuning methods. ## 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 P-tuning-v2 - Avoid using P-tuning v2 when you have large-scale datasets and model architectures, as it might not be as effective compared to full fine-tuning methods. - If your project requires a high level of customization beyond prompt tuning or continuous prompts do not align with the problem complexity. ## Common questions ### What is the difference between gpt-neox and P-tuning-v2? gpt-neox: Implementation of model parallel autoregressive transformers on GPUs based on Megatron and DeepSpeed libraries. P-tuning-v2: Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpt-neox over P-tuning-v2? Choose gpt-neox over P-tuning-v2 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, 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 P-tuning-v2 over gpt-neox? Choose P-tuning-v2 over gpt-neox when Requirements: Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance.; Tags unique to P-tuning-v2: natural-language-processing, p-tuning, parameter-efficient-learning, pretrained-language-model; For scenarios requiring efficient use of resources, as P-tuning v2 requires fewer parameters than traditional fine-tuning methods. ### 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 P-tuning-v2? Avoid using P-tuning v2 when you have large-scale datasets and model architectures, as it might not be as effective compared to full fine-tuning methods. If your project requires a high level of customization beyond prompt tuning or continuous prompts do not align with the problem complexity. ### Is gpt-neox or P-tuning-v2 more popular on GitHub? gpt-neox has more GitHub stars (7,452 vs 2,077). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt-neox and P-tuning-v2 open source? Yes - both are open-source projects on GitHub (gpt-neox: Apache-2.0, P-tuning-v2: Apache-2.0). ### Where can I find alternatives to gpt-neox or P-tuning-v2? GraphCanon lists graph-backed alternatives at [gpt-neox alternatives](/tools/eleutherai-gpt-neox/alternatives) and [P-tuning-v2 alternatives](/tools/thudm-p-tuning-v2/alternatives) ([gpt-neox markdown twin](/tools/eleutherai-gpt-neox/alternatives.md), [P-tuning-v2 markdown twin](/tools/thudm-p-tuning-v2/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-thudm-p-tuning-v2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpt-neox or P-tuning-v2? gpt-neox: Steady. P-tuning-v2: Dormant. 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 P-tuning-v2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gpt-neox trust report](/tools/eleutherai-gpt-neox/trust); [P-tuning-v2 trust report](/tools/thudm-p-tuning-v2/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/_