--- title: "litgpt vs TensorRT-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lightning-ai-litgpt-vs-nvidia-tensorrt-llm" tools: ["lightning-ai-litgpt", "nvidia-tensorrt-llm"] --- # litgpt vs TensorRT-LLM *GraphCanon updated Jul 12, 2026* ## Verdict Pick litgpt if litGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment; pick TensorRT-LLM if `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. [litgpt](https://lightning.ai) reports 13k GitHub stars, 1.5k forks, and 267 open issues, last pushed Jul 6, 2026. [TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM) has 14k stars, 2.5k forks, and 1.5k open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [litgpt's repository](https://github.com/Lightning-AI/litgpt) and [TensorRT-LLM's repository](https://github.com/NVIDIA/TensorRT-LLM). | | [litgpt](/tools/lightning-ai-litgpt.md) | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | | --- | --- | --- | | Tagline | High-performance LLMs with recipes for pretraining, finetuning and deployment | Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs | | Stars | 13,473 | 14,091 | | Forks | 1,468 | 2,547 | | Open issues | 267 | 1,500 | | Language | Python | Python | | Adopt for | LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment. | `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. | | Persona | - | - | | Runtime | - | - | | License | LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification. | Other | | Categories | LLM Frameworks, Model Training, Inference & Serving | LLM Frameworks, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [litgpt](/tools/lightning-ai-litgpt.md) | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | | --- | --- | --- | | Days since push | 4d | 0d | | Open issues (now) | 267 | 1.5k | | Security scan | No lockfile | 16 low (16 low) | | Full report | [trust report](/tools/lightning-ai-litgpt/trust.md) | [trust report](/tools/nvidia-tensorrt-llm/trust.md) | ## Decision facts: litgpt - **Pricing:** freemium - The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models. - **Requirements:** Min 16 GB RAM - **Adopt for:** LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment. - **License detail:** LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification. ## Decision facts: TensorRT-LLM - **Pricing:** oss - Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions. - **Requirements:** NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities. - **Adopt for:** `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. ## Choose when ### Choose litgpt if… - License: litgpt is Apache-2.0, TensorRT-LLM is Other. - Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.. - Requirements: Min 16 GB RAM. - Tags unique to litgpt: llms, deep-learning, ai, artificial-intelligence. - Also covers Model Training. - If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application. ### Choose TensorRT-LLM if… - License: TensorRT-LLM is Other, litgpt is Apache-2.0. - Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.. - Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.. - Tags unique to TensorRT-LLM: moe, cuda, llm-serving, pytorch. - When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware. ## When NOT to use litgpt - If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. - When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable. ## When NOT to use TensorRT-LLM - When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. - If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. - For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions. ## Common questions ### What is the difference between litgpt and TensorRT-LLM? litgpt: High-performance LLMs with recipes for pretraining, finetuning and deployment. TensorRT-LLM: Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs. See the comparison table for live GitHub stats and shared categories. ### When should I choose litgpt over TensorRT-LLM? Choose litgpt over TensorRT-LLM when License: litgpt is Apache-2.0, TensorRT-LLM is Other; Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.; Requirements: Min 16 GB RAM; Tags unique to litgpt: llms, deep-learning, ai, artificial-intelligence; Also covers Model Training; If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application. ### When should I choose TensorRT-LLM over litgpt? Choose TensorRT-LLM over litgpt when License: TensorRT-LLM is Other, litgpt is Apache-2.0; Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.; Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.; Tags unique to TensorRT-LLM: moe, cuda, llm-serving, pytorch; When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware. ### When should I avoid litgpt? If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable. ### When should I avoid TensorRT-LLM? When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions. ### Is litgpt or TensorRT-LLM more popular on GitHub? TensorRT-LLM has more GitHub stars (14,091 vs 13,473). Stars measure visibility, not whether either tool fits your constraints. ### Are litgpt and TensorRT-LLM open source? Yes - both are open-source projects on GitHub (litgpt: Apache-2.0, TensorRT-LLM: Other). ### Where can I find alternatives to litgpt or TensorRT-LLM? GraphCanon lists graph-backed alternatives at [litgpt alternatives](/tools/lightning-ai-litgpt/alternatives) and [TensorRT-LLM alternatives](/tools/nvidia-tensorrt-llm/alternatives) ([litgpt markdown twin](/tools/lightning-ai-litgpt/alternatives.md), [TensorRT-LLM markdown twin](/tools/nvidia-tensorrt-llm/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-nvidia-tensorrt-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, litgpt or TensorRT-LLM? litgpt: Very active. TensorRT-LLM: 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 litgpt and TensorRT-LLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litgpt trust report](/tools/lightning-ai-litgpt/trust); [TensorRT-LLM trust report](/tools/nvidia-tensorrt-llm/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/_