--- title: "llama.cpp vs CTranslate2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ggml-org-llama-cpp-vs-opennmt-ctranslate2" tools: ["ggml-org-llama-cpp", "opennmt-ctranslate2"] --- # llama.cpp vs CTranslate2 *GraphCanon updated Sep 20, 2026* ## Verdict Pick llama.cpp if llama.cpp is a C++ tool for performing inference with large language models, offering a lightweight and efficient alternative to Python-based solutions; pick CTranslate2 if cTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++. [llama.cpp](https://llama.app) reports 129k GitHub stars, 23k forks, and 2.5k 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 [llama.cpp's repository](https://github.com/ggml-org/llama.cpp) and [CTranslate2's repository](https://github.com/OpenNMT/CTranslate2). | | [llama.cpp](/tools/ggml-org-llama-cpp.md) | [CTranslate2](/tools/opennmt-ctranslate2.md) | | --- | --- | --- | | Tagline | LLM inference in C/C++ | Fast inference engine for Transformer models | | Stars | 128,639 | 4,677 | | Forks | 23,359 | 534 | | Open issues | 2,491 | 286 | | Language | C++ | C++ | | Adopt for | llama.cpp is a C++ tool for performing inference with large language models, offering a lightweight and efficient alternative to Python-based solutions. | CTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++. | | Persona | - | - | | Runtime | - | - | | License | MIT | CTranslate2 is available under the MIT license, allowing for free use and modification. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llama.cpp](/tools/ggml-org-llama-cpp.md) | [CTranslate2](/tools/opennmt-ctranslate2.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 17d | | Open issues (now) | 2.5k | 286 | | Stars delta | +5.7k (30d) | +73 (30d) | | Open issues delta | +522 (30d) | +9 (30d) | | Full report | [trust report](/tools/ggml-org-llama-cpp/trust.md) | [trust report](/tools/opennmt-ctranslate2/trust.md) | ## Decision facts: llama.cpp - **Adopt for:** llama.cpp is a C++ tool for performing inference with large language models, offering a lightweight and efficient alternative to Python-based solutions. ## 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 llama.cpp if… - Tags unique to llama.cpp: c++, ggml, llm-inference. - When you need a fast and lightweight solution for LLM inference that can be integrated into C++ projects. - More GitHub stars (129k vs 4.7k) - visibility, not fit. ### Choose CTranslate2 if… - 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, cuda. - When you need fast inference for Transformer models with support for hardware accelerations like AVX, AVX2, CUDA, and ROCm. ## When NOT to use llama.cpp - If your project is already heavily invested in Python and you do not want to introduce another language for inference. - When you need extensive model training capabilities, as llama.cpp focuses solely on inference. - If you are looking for a tool with a large community and extensive documentation, as llama.cpp might have a smaller community and less comprehensive support compared to some Python-based alternatives. ## 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 llama.cpp and CTranslate2? llama.cpp: LLM inference in C/C++. CTranslate2: Fast inference engine for Transformer models. See the comparison table for live GitHub stats and shared categories. ### When should I choose llama.cpp over CTranslate2? Choose llama.cpp over CTranslate2 when Tags unique to llama.cpp: c++, ggml, llm-inference; When you need a fast and lightweight solution for LLM inference that can be integrated into C++ projects; More GitHub stars (129k vs 4.7k) - visibility, not fit. ### When should I choose CTranslate2 over llama.cpp? Choose CTranslate2 over llama.cpp when 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, cuda; When you need fast inference for Transformer models with support for hardware accelerations like AVX, AVX2, CUDA, and ROCm. ### When should I avoid llama.cpp? If your project is already heavily invested in Python and you do not want to introduce another language for inference. When you need extensive model training capabilities, as llama.cpp focuses solely on inference. If you are looking for a tool with a large community and extensive documentation, as llama.cpp might have a smaller community and less comprehensive support compared to some Python-based alternatives. ### 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 llama.cpp or CTranslate2 more popular on GitHub? llama.cpp has more GitHub stars (128,639 vs 4,677). Stars measure visibility, not whether either tool fits your constraints. ### Are llama.cpp and CTranslate2 open source? Yes - both are open-source projects on GitHub (llama.cpp: MIT, CTranslate2: MIT). ### Where can I find alternatives to llama.cpp or CTranslate2? GraphCanon lists graph-backed alternatives at [llama.cpp alternatives](/tools/ggml-org-llama-cpp/alternatives) and [CTranslate2 alternatives](/tools/opennmt-ctranslate2/alternatives) ([llama.cpp markdown twin](/tools/ggml-org-llama-cpp/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/ggml-org-llama-cpp-vs-opennmt-ctranslate2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llama.cpp or CTranslate2? llama.cpp: 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 llama.cpp and CTranslate2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llama.cpp trust report](/tools/ggml-org-llama-cpp/trust); [CTranslate2 trust report](/tools/opennmt-ctranslate2/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ggml-org-llama-cpp`](/api/graphcanon/graph?tool=ggml-org-llama-cpp) - 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/_