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Comparison

llama.cpp vs CTranslate2

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++.

Markdown twin · llama.cpp alternatives · CTranslate2 alternatives

GraphCanon updated Sep 20, 2026

10views this month

llama.cpp logo

llama.cpp

ggml-org/llama.cpp

★ 129kpushed Sep 18, 2026
vs
CTranslate2 logo

CTranslate2

OpenNMT/CTranslate2

★ 4.7kpushed Aug 31, 2026

Trust & integrity

Signalllama.cppCTranslate2
Maintenance
Very active (0d since push)
As of Sep 18, 2026 · github_public_v1
Active (17d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No published findings from this source as of 2026-09-18
As of Sep 18, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Published findings
As of Oct 11, 2026 · openssf-scorecard@v1
llama.cpp trust reportCTranslate2 trust reportTrust methodology

Tagline

llama.cpp
LLM inference in C/C++
CTranslate2
Fast inference engine for Transformer models

Stars

llama.cpp
129k
CTranslate2
4.7k

Forks

llama.cpp
23k
CTranslate2
534

Open issues

llama.cpp
2.5k
CTranslate2
286

Language

llama.cpp
C++
CTranslate2
C++

Adopt for

llama.cpp
llama.cpp is a C++ tool for performing inference with large language models, offering a lightweight and efficient alternative to Python-based solutions.
CTranslate2
CTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++.

Persona

llama.cpp
-
CTranslate2
-

Runtime

llama.cpp
-
CTranslate2
-

License

llama.cpp
MIT
CTranslate2
CTranslate2 is available under the MIT license, allowing for free use and modification.

Last pushed

llama.cpp
Sep 18, 2026
CTranslate2
Aug 31, 2026

Categories

llama.cpp
Inference & Serving
CTranslate2
Inference & Serving

Trust and health

Maintenance

llama.cpp
Very active (96%)
CTranslate2
Active (82%)

Days since push

llama.cpp
0d
CTranslate2
17d

Open issues (now)

llama.cpp
2.5k
CTranslate2
286

Stars delta

llama.cpp
+5.7k (30d)
CTranslate2
+73 (30d)

Open issues delta

llama.cpp
+522 (30d)
CTranslate2
+9 (30d)

OSV dependency advisories

llama.cpp
No published findings from this source as of 2026-09-18
CTranslate2
No lockfile (source not queried)

OpenSSF Scorecard

llama.cpp
Not queried
CTranslate2
Published findings

Full report

llama.cpp
Trust report
CTranslate2
Trust report

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.

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.
GitHubDetails

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 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.
GitHubDetails

Explore

llama.cpp trust report →CTranslate2 trust report →Inference & Serving category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: llama.cpp 129k · CTranslate2 4.7k (synced Sep 20, 2026).

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 and CTranslate2 alternatives (llama.cpp markdown twin, CTranslate2 markdown twin), 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 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; CTranslate2 trust report.

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