--- title: "TensorRT-LLM vs CTranslate2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-tensorrt-llm-vs-opennmt-ctranslate2" tools: ["nvidia-tensorrt-llm", "opennmt-ctranslate2"] --- # TensorRT-LLM vs CTranslate2 *GraphCanon updated Sep 20, 2026* ## Verdict Pick TensorRT-LLM if tensorRT-LLM is a Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs, offering components for creating Python and C++ runtimes for performant inference execution; pick CTranslate2 if cTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++. [TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM) reports 15k GitHub stars, 2.8k forks, and 1.5k open issues, last pushed Sep 18, 2026. [CTranslate2](https://opennmt.net/CTranslate2) has 4.7k stars, 534 forks, and 286 open issues, last pushed Aug 31, 2026. Figures are from public GitHub metadata via [TensorRT-LLM's repository](https://github.com/NVIDIA/TensorRT-LLM) and [CTranslate2's repository](https://github.com/OpenNMT/CTranslate2). | | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | [CTranslate2](/tools/opennmt-ctranslate2.md) | | --- | --- | --- | | Tagline | Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs | Fast inference engine for Transformer models | | Stars | 14,645 | 4,677 | | Forks | 2,756 | 534 | | Open issues | 1,510 | 286 | | Language | Python | C++ | | Adopt for | TensorRT-LLM is a Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs, offering components for creating Python and C++ runtimes for performant inference execution. | CTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++. | | Persona | - | - | | Runtime | - | - | | License | Other | CTranslate2 is available under the MIT license, allowing for free use and modification. | | Categories | Inference & Serving, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | [CTranslate2](/tools/opennmt-ctranslate2.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 17d | | Open issues (now) | 1.5k | 286 | | Stars delta | +328 (30d) | +73 (30d) | | Open issues delta | -106 (30d) | +9 (30d) | | Full report | [trust report](/tools/nvidia-tensorrt-llm/trust.md) | [trust report](/tools/opennmt-ctranslate2/trust.md) | ## Decision facts: TensorRT-LLM - **Adopt for:** TensorRT-LLM is a Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs, offering components for creating Python and C++ runtimes for performant inference execution. ## Decision facts: CTranslate2 - **Requirements:** Min 2 GB RAM; CTranslate2 can be installed via pip, and specific Python wheels are available for AMD ROCm GPUs. - **Adopt for:** CTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++. - **License detail:** CTranslate2 is available under the MIT license, allowing for free use and modification. ## Choose when ### Choose TensorRT-LLM if… - TensorRT-LLM is primarily Python; CTranslate2 is C++. - License: TensorRT-LLM is Other, CTranslate2 is MIT. - Tags unique to TensorRT-LLM: blackwell, llm-serving, moe, pytorch. - Also covers Model Training. - When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs. ### Choose CTranslate2 if… - CTranslate2 is primarily C++; TensorRT-LLM is Python. - License: CTranslate2 is MIT, TensorRT-LLM is Other. - Requirements: Min 2 GB RAM; CTranslate2 can be installed via pip, and specific Python wheels are available for AMD ROCm GPUs.. - Tags unique to CTranslate2: avx, avx2, cpp, deep-learning. - When you need fast inference for Transformer models with support for hardware accelerations like AVX, AVX2, CUDA, and ROCm. ## When NOT to use TensorRT-LLM - If your hardware does not include NVIDIA GPUs, as TensorRT-LLM is specifically optimized for NVIDIA GPUs. - If you do not require a Python API for defining and optimizing LLMs, and prefer a different interface or tool. - When you are working in a non-NVIDIA GPU environment, as TensorRT-LLM's optimizations are tailored to NVIDIA hardware. ## When NOT to use CTranslate2 - If your hardware does not support the specific accelerations CTranslate2 offers, such as AVX, AVX2, CUDA, or ROCm. - When you prefer a tool that is not written in C++ and does not require a C++ runtime environment. - If your project does not benefit from the specific optimizations like intrinsics, GEMM, or quantization that CTranslate2 provides. ## Common questions ### What is the difference between TensorRT-LLM and CTranslate2? TensorRT-LLM: Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs. CTranslate2: Fast inference engine for Transformer models. See the comparison table for live GitHub stats and shared categories. ### When should I choose TensorRT-LLM over CTranslate2? Choose TensorRT-LLM over CTranslate2 when TensorRT-LLM is primarily Python; CTranslate2 is C++; License: TensorRT-LLM is Other, CTranslate2 is MIT; Tags unique to TensorRT-LLM: blackwell, llm-serving, moe, pytorch; Also covers Model Training; When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs. ### When should I choose CTranslate2 over TensorRT-LLM? Choose CTranslate2 over TensorRT-LLM when CTranslate2 is primarily C++; TensorRT-LLM is Python; License: CTranslate2 is MIT, TensorRT-LLM is Other; Requirements: Min 2 GB RAM; CTranslate2 can be installed via pip, and specific Python wheels are available for AMD ROCm GPUs.; Tags unique to CTranslate2: avx, avx2, cpp, deep-learning; When you need fast inference for Transformer models with support for hardware accelerations like AVX, AVX2, CUDA, and ROCm. ### When should I avoid TensorRT-LLM? If your hardware does not include NVIDIA GPUs, as TensorRT-LLM is specifically optimized for NVIDIA GPUs. If you do not require a Python API for defining and optimizing LLMs, and prefer a different interface or tool. When you are working in a non-NVIDIA GPU environment, as TensorRT-LLM's optimizations are tailored to NVIDIA hardware. ### When should I avoid CTranslate2? If your hardware does not support the specific accelerations CTranslate2 offers, such as AVX, AVX2, CUDA, or ROCm. When you prefer a tool that is not written in C++ and does not require a C++ runtime environment. If your project does not benefit from the specific optimizations like intrinsics, GEMM, or quantization that CTranslate2 provides. ### Is TensorRT-LLM or CTranslate2 more popular on GitHub? TensorRT-LLM has more GitHub stars (14,645 vs 4,677). Stars measure visibility, not whether either tool fits your constraints. ### Are TensorRT-LLM and CTranslate2 open source? Yes - both are open-source projects on GitHub (TensorRT-LLM: Other, CTranslate2: MIT). ### Where can I find alternatives to TensorRT-LLM or CTranslate2? GraphCanon lists graph-backed alternatives at [TensorRT-LLM alternatives](/tools/nvidia-tensorrt-llm/alternatives) and [CTranslate2 alternatives](/tools/opennmt-ctranslate2/alternatives) ([TensorRT-LLM markdown twin](/tools/nvidia-tensorrt-llm/alternatives.md), [CTranslate2 markdown twin](/tools/opennmt-ctranslate2/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/nvidia-tensorrt-llm-vs-opennmt-ctranslate2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, TensorRT-LLM or CTranslate2? TensorRT-LLM: Very active. CTranslate2: 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 TensorRT-LLM and CTranslate2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TensorRT-LLM trust report](/tools/nvidia-tensorrt-llm/trust); [CTranslate2 trust report](/tools/opennmt-ctranslate2/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-tensorrt-llm`](/api/graphcanon/graph?tool=nvidia-tensorrt-llm) - 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/_