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Comparison

TensorRT-LLM vs CTranslate2

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++.

Markdown twin · TensorRT-LLM alternatives · CTranslate2 alternatives

GraphCanon updated Sep 20, 2026

9views this month

TensorRT-LLM logo

TensorRT-LLM

NVIDIA/TensorRT-LLM

★ 15kpushed Sep 18, 2026
vs
CTranslate2 logo

CTranslate2

OpenNMT/CTranslate2

★ 4.7kpushed Aug 31, 2026

Trust & integrity

SignalTensorRT-LLMCTranslate2
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
Published findings
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
TensorRT-LLM trust reportCTranslate2 trust reportTrust methodology

Tagline

TensorRT-LLM
Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs
CTranslate2
Fast inference engine for Transformer models

Stars

TensorRT-LLM
15k
CTranslate2
4.7k

Forks

TensorRT-LLM
2.8k
CTranslate2
534

Open issues

TensorRT-LLM
1.5k
CTranslate2
286

Language

TensorRT-LLM
Python
CTranslate2
C++

Adopt for

TensorRT-LLM
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
CTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++.

Persona

TensorRT-LLM
-
CTranslate2
-

Runtime

TensorRT-LLM
-
CTranslate2
-

License

TensorRT-LLM
Other
CTranslate2
CTranslate2 is available under the MIT license, allowing for free use and modification.

Last pushed

TensorRT-LLM
Sep 18, 2026
CTranslate2
Aug 31, 2026

Categories

TensorRT-LLM
Inference & Serving, Model Training
CTranslate2
Inference & Serving

Trust and health

Maintenance

TensorRT-LLM
Very active (96%)
CTranslate2
Active (82%)

Days since push

TensorRT-LLM
0d
CTranslate2
17d

Open issues (now)

TensorRT-LLM
1.5k
CTranslate2
286

Stars delta

TensorRT-LLM
+328 (30d)
CTranslate2
+73 (30d)

Open issues delta

TensorRT-LLM
-106 (30d)
CTranslate2
+9 (30d)

OSV dependency advisories

TensorRT-LLM
Published findings
CTranslate2
No lockfile (source not queried)

OpenSSF Scorecard

TensorRT-LLM
Not queried
CTranslate2
Published findings

Full report

TensorRT-LLM
Trust report
CTranslate2
Trust report

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.

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.
GitHubDetails

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 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

TensorRT-LLM trust report →CTranslate2 trust report →Inference & Serving category →Model Training 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: TensorRT-LLM 15k · CTranslate2 4.7k (synced Sep 20, 2026).

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 and CTranslate2 alternatives (TensorRT-LLM 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, 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; CTranslate2 trust report.

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