Comparison
tiny-vllm vs CTranslate2
Verdict
Pick tiny-vllm if for those needing a compact yet potent LLM inference engine built on C++ and CUDA, tiny-vllm presents an accessible framework inspired by its larger sibling, vLLM; pick CTranslate2 if cTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++.
Markdown twin · tiny-vllm alternatives · CTranslate2 alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | tiny-vllm | CTranslate2 |
|---|---|---|
| Maintenance | Very active (3d since push) As of Sep 19, 2026 · github_public_v1 | Active (17d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 19, 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
- tiny-vllm
- Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM
- CTranslate2
- Fast inference engine for Transformer models
Stars
- tiny-vllm
- 1.1k
- CTranslate2
- 4.7k
Forks
- tiny-vllm
- 92
- CTranslate2
- 534
Open issues
- tiny-vllm
- 1
- CTranslate2
- 286
Language
- tiny-vllm
- C++
- CTranslate2
- C++
Adopt for
- tiny-vllm
- For those needing a compact yet potent LLM inference engine built on C++ and CUDA, tiny-vllm presents an accessible framework inspired by its larger sibling, vLLM.
- CTranslate2
- CTranslate2 is a fast inference engine for Transformer models, optimized for various hardware accelerations and written in C++.
Persona
- tiny-vllm
- -
- CTranslate2
- -
Runtime
- tiny-vllm
- -
- CTranslate2
- -
License
- tiny-vllm
- Apache-2.0
- CTranslate2
- CTranslate2 is available under the MIT license, allowing for free use and modification.
Last pushed
- tiny-vllm
- Sep 15, 2026
- CTranslate2
- Aug 31, 2026
Categories
- tiny-vllm
- Inference & Serving
- CTranslate2
- Inference & Serving
Trust and health
Maintenance
- tiny-vllm
- Very active (96%)
- CTranslate2
- Active (82%)
Days since push
- tiny-vllm
- 3d
- CTranslate2
- 17d
Open issues (now)
- tiny-vllm
- 1
- CTranslate2
- 286
Stars delta
- tiny-vllm
- +173 (30d)
- CTranslate2
- +73 (30d)
Open issues delta
- tiny-vllm
- -1 (30d)
- CTranslate2
- +9 (30d)
Owner type
- tiny-vllm
- User
- CTranslate2
- Organization
OpenSSF Scorecard
- tiny-vllm
- Not queried
- CTranslate2
- Published findings
Full report
- tiny-vllm
- Trust report
- CTranslate2
- Trust report
Shared compatibility
- Python · tiny-vllm: Python runtime · CTranslate2: Python runtime
Choose tiny-vllm if…
- License: tiny-vllm is Apache-2.0, CTranslate2 is MIT.
- Tags unique to tiny-vllm: hpc, llm, lstm.
- When you require a lightweight solution for deploying large language model inference in environments with limited resources but still demand high performance.
When NOT to use tiny-vllm
- Avoid using tiny-vllm if the application requires the full feature set offered by its larger counterpart, vLLM, as it has been trimmed for lightweight use.
- Do not choose this tool when working in environments that do not support CUDA or where a higher abstraction level is preferred over direct C++ and CUDA implementation.
Choose CTranslate2 if…
- License: CTranslate2 is MIT, tiny-vllm 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 (jmaczan/tiny-vllm) · observed Sep 20, 2026
- GitHub forks (jmaczan/tiny-vllm) · observed Sep 20, 2026
- Last push (jmaczan/tiny-vllm) · observed Sep 15, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 15, 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: tiny-vllm 1.1k · CTranslate2 4.7k (synced Sep 20, 2026).
Common questions
- What is the difference between tiny-vllm and CTranslate2?
- tiny-vllm: Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM. CTranslate2: Fast inference engine for Transformer models. See the comparison table for live GitHub stats and shared categories.
- When should I choose tiny-vllm over CTranslate2?
- Choose tiny-vllm over CTranslate2 when License: tiny-vllm is Apache-2.0, CTranslate2 is MIT; Tags unique to tiny-vllm: hpc, llm, lstm; When you require a lightweight solution for deploying large language model inference in environments with limited resources but still demand high performance.
- When should I choose CTranslate2 over tiny-vllm?
- Choose CTranslate2 over tiny-vllm when License: CTranslate2 is MIT, tiny-vllm 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 tiny-vllm?
- Avoid using tiny-vllm if the application requires the full feature set offered by its larger counterpart, vLLM, as it has been trimmed for lightweight use. Do not choose this tool when working in environments that do not support CUDA or where a higher abstraction level is preferred over direct C++ and CUDA implementation.
- 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 tiny-vllm or CTranslate2 more popular on GitHub?
- CTranslate2 has more GitHub stars (4,677 vs 1,120). Stars measure visibility, not whether either tool fits your constraints.
- Are tiny-vllm and CTranslate2 open source?
- Yes - both are open-source projects on GitHub (tiny-vllm: Apache-2.0, CTranslate2: MIT).
- Where can I find alternatives to tiny-vllm or CTranslate2?
- GraphCanon lists graph-backed alternatives at tiny-vllm alternatives and CTranslate2 alternatives (tiny-vllm 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, tiny-vllm or CTranslate2?
- tiny-vllm: 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 tiny-vllm and CTranslate2?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: tiny-vllm trust report; CTranslate2 trust report.