--- title: "litgpt vs P-tuning-v2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lightning-ai-litgpt-vs-thudm-p-tuning-v2" tools: ["lightning-ai-litgpt", "thudm-p-tuning-v2"] --- # litgpt vs P-tuning-v2 *GraphCanon updated Sep 26, 2026* ## Verdict Pick litgpt if litgpt offers a suite of over 20 high-performance large language models, with tools for pretraining, finetuning, and deployment at scale, all under the 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 compared. [litgpt](https://lightning.ai) reports 14k GitHub stars, 1.5k forks, and 290 open issues, last pushed Sep 14, 2026. [P-tuning-v2](https://github.com/THUDM/P-tuning-v2) has 2.1k stars, 212 forks, and 35 open issues, last pushed Nov 16, 2023. Figures are from public GitHub metadata via [litgpt's repository](https://github.com/Lightning-AI/litgpt) and [P-tuning-v2's repository](https://github.com/THUDM/P-tuning-v2). | | [litgpt](/tools/lightning-ai-litgpt.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | | --- | --- | --- | | Tagline | High-performance LLMs for pretraining, finetuning, and deployment | Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks | | Stars | 13,667 | 2,080 | | Forks | 1,503 | 212 | | Open issues | 290 | 35 | | Language | Python | Python | | Adopt for | litgpt offers a suite of over 20 high-performance large language models, with tools for pretraining, finetuning, and deployment at scale, all under the 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 | Apache-2.0 | 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 | Inference & Serving, LLM Frameworks, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [litgpt](/tools/lightning-ai-litgpt.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 3d | 1045d | | Open issues (now) | 290 | 35 | | Stars delta | +62 (30d) | +3 (30d) | | Open issues delta | +18 (30d) | 0 (30d) | | Full report | [trust report](/tools/lightning-ai-litgpt/trust.md) | [trust report](/tools/thudm-p-tuning-v2/trust.md) | ## Shared compatibility - **Python**: [litgpt](/tools/lightning-ai-litgpt.md) - Python runtime; [P-tuning-v2](/tools/thudm-p-tuning-v2.md) - Python runtime ## Decision facts: litgpt - **Adopt for:** litgpt offers a suite of over 20 high-performance large language models, with tools for pretraining, finetuning, and deployment at scale, all under the Apache-2.0 license. ## 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 litgpt if… - Tags unique to litgpt: ai, artificial-intelligence, deep-learning, large-language-models. - Also covers Inference & Serving, LLM Frameworks. - When you need a comprehensive set of over 20 high-performance LLMs for pretraining, finetuning, and deployment. ### 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 litgpt - If you are looking for a tool that supports languages other than Python, as litgpt is exclusively Python-based. - When you need a tool that offers fewer model options, as litgpt provides over 20 models which might be overwhelming for specific use cases. - If you require proprietary licensing, as litgpt is open-source under the Apache-2.0 license. ## 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 litgpt and P-tuning-v2? litgpt: High-performance LLMs for pretraining, finetuning, and deployment. 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 litgpt over P-tuning-v2? Choose litgpt over P-tuning-v2 when Tags unique to litgpt: ai, artificial-intelligence, deep-learning, large-language-models; Also covers Inference & Serving, LLM Frameworks; When you need a comprehensive set of over 20 high-performance LLMs for pretraining, finetuning, and deployment. ### When should I choose P-tuning-v2 over litgpt? Choose P-tuning-v2 over litgpt 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 litgpt? If you are looking for a tool that supports languages other than Python, as litgpt is exclusively Python-based. When you need a tool that offers fewer model options, as litgpt provides over 20 models which might be overwhelming for specific use cases. If you require proprietary licensing, as litgpt is open-source under the Apache-2.0 license. ### 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 litgpt or P-tuning-v2 more popular on GitHub? litgpt has more GitHub stars (13,667 vs 2,080). Stars measure visibility, not whether either tool fits your constraints. ### Are litgpt and P-tuning-v2 open source? Yes - both are open-source projects on GitHub (litgpt: Apache-2.0, P-tuning-v2: Apache-2.0). ### Where can I find alternatives to litgpt or P-tuning-v2? GraphCanon lists graph-backed alternatives at [litgpt alternatives](/tools/lightning-ai-litgpt/alternatives) and [P-tuning-v2 alternatives](/tools/thudm-p-tuning-v2/alternatives) ([litgpt markdown twin](/tools/lightning-ai-litgpt/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/lightning-ai-litgpt-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, litgpt or P-tuning-v2? litgpt: Very active. 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 litgpt and P-tuning-v2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litgpt trust report](/tools/lightning-ai-litgpt/trust); [P-tuning-v2 trust report](/tools/thudm-p-tuning-v2/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lightning-ai-litgpt`](/api/graphcanon/graph?tool=lightning-ai-litgpt) - 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/_