Comparison
FasterTransformer vs CTranslate2
Verdict
Pick FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch; pick CTranslate2 if cTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++.
Markdown twin · FasterTransformer alternatives · CTranslate2 alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | FasterTransformer | CTranslate2 |
|---|---|---|
| Maintenance | Dormant (893d since push) As of Sep 7, 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 7, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 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 |
Tagline
- FasterTransformer
- Transformer related optimization including BERT and GPT
- CTranslate2
- Fast inference engine for Transformer models
Stars
- FasterTransformer
- 6.4k
- CTranslate2
- 4.7k
Forks
- FasterTransformer
- 934
- CTranslate2
- 534
Open issues
- FasterTransformer
- 289
- CTranslate2
- 286
Language
- FasterTransformer
- C++
- CTranslate2
- C++
Adopt for
- FasterTransformer
- Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch.
- CTranslate2
- CTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++.
Persona
- FasterTransformer
- -
- CTranslate2
- -
Runtime
- FasterTransformer
- -
- CTranslate2
- -
License
- FasterTransformer
- Apache-2.0
- CTranslate2
- CTranslate2 is available under the MIT license, allowing for free use and modification.
Last pushed
- FasterTransformer
- Mar 27, 2024
- CTranslate2
- Aug 31, 2026
Categories
- FasterTransformer
- Inference & Serving
- CTranslate2
- Inference & Serving
Trust and health
Maintenance
- FasterTransformer
- Dormant (18%)
- CTranslate2
- Active (82%)
Days since push
- FasterTransformer
- 893d
- CTranslate2
- 17d
Open issues (now)
- FasterTransformer
- 289
- CTranslate2
- 286
Stars delta
- FasterTransformer
- +2 (30d)
- CTranslate2
- +73 (30d)
Open issues delta
- FasterTransformer
- 0 (30d)
- CTranslate2
- +9 (30d)
OpenSSF Scorecard
- FasterTransformer
- Not queried
- CTranslate2
- Published findings
Full report
- FasterTransformer
- Trust report
- CTranslate2
- Trust report
Choose FasterTransformer if…
- License: FasterTransformer is Apache-2.0, CTranslate2 is MIT.
- Tags unique to FasterTransformer: bert, cublas, cublaslt, gpt.
- When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically.
When NOT to use FasterTransformer
- If looking for active development and latest improvements on LLM Inference as NVIDIA recommends TensorRT-LLM over FasterTransformer now.
- When specific frameworks not including TensorFlow, PyTorch, or Triton are required.
Choose CTranslate2 if…
- License: CTranslate2 is MIT, FasterTransformer is Apache-2.0.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (NVIDIA/FasterTransformer) · observed Sep 20, 2026
- GitHub forks (NVIDIA/FasterTransformer) · observed Sep 20, 2026
- Last push (NVIDIA/FasterTransformer) · observed Mar 27, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (OpenNMT/CTranslate2) · observed Sep 20, 2026
- GitHub forks (OpenNMT/CTranslate2) · observed Sep 20, 2026
- Last push (OpenNMT/CTranslate2) · observed Aug 31, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: FasterTransformer 6.4k · CTranslate2 4.7k (synced Sep 20, 2026).
Common questions
- What is the difference between FasterTransformer and CTranslate2?
- FasterTransformer: Transformer related optimization including BERT and GPT. CTranslate2: Fast inference engine for Transformer models. See the comparison table for live GitHub stats and shared categories.
- When should I choose FasterTransformer over CTranslate2?
- Choose FasterTransformer over CTranslate2 when License: FasterTransformer is Apache-2.0, CTranslate2 is MIT; Tags unique to FasterTransformer: bert, cublas, cublaslt, gpt; When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically.
- When should I choose CTranslate2 over FasterTransformer?
- Choose CTranslate2 over FasterTransformer when License: CTranslate2 is MIT, FasterTransformer is Apache-2.0; 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 FasterTransformer?
- If looking for active development and latest improvements on LLM Inference as NVIDIA recommends TensorRT-LLM over FasterTransformer now. When specific frameworks not including TensorFlow, PyTorch, or Triton are required.
- 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 FasterTransformer or CTranslate2 more popular on GitHub?
- FasterTransformer has more GitHub stars (6,448 vs 4,677). Stars measure visibility, not whether either tool fits your constraints.
- Are FasterTransformer and CTranslate2 open source?
- Yes - both are open-source projects on GitHub (FasterTransformer: Apache-2.0, CTranslate2: MIT).
- Where can I find alternatives to FasterTransformer or CTranslate2?
- GraphCanon lists graph-backed alternatives at FasterTransformer alternatives and CTranslate2 alternatives (FasterTransformer 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, FasterTransformer or CTranslate2?
- FasterTransformer: Dormant. 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 FasterTransformer and CTranslate2?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FasterTransformer trust report; CTranslate2 trust report.