Comparison
llama.cpp vs vllm
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 vllm if vLLM is a Python-based engine for efficient inference and serving of large language models, offering high throughput and memory efficiency.
Markdown twin · llama.cpp alternatives · vllm alternatives
GraphCanon updated Sep 20, 2026
14views this month
Trust & integrity
| Signal | llama.cpp | vllm |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d 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 | Not queried openssf-scorecard@v1 |
Tagline
- llama.cpp
- LLM inference in C/C++
- vllm
- A high-throughput and memory-efficient inference and serving engine for LLMs
Stars
- llama.cpp
- 129k
- vllm
- 92k
Forks
- llama.cpp
- 23k
- vllm
- 22k
Open issues
- llama.cpp
- 2.5k
- vllm
- 8.1k
Language
- llama.cpp
- C++
- vllm
- Python
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.
- vllm
- vLLM is a Python-based engine for efficient inference and serving of large language models, offering high throughput and memory efficiency.
Persona
- llama.cpp
- -
- vllm
- -
Runtime
- llama.cpp
- -
- vllm
- -
License
- llama.cpp
- MIT
- vllm
- vLLM is licensed under the Apache-2.0 license, allowing for broad use in both open-source and commercial projects.
Last pushed
- llama.cpp
- Sep 18, 2026
- vllm
- Sep 18, 2026
Categories
- llama.cpp
- Inference & Serving
- vllm
- Inference & Serving
Trust and health
Open issues (now)
- llama.cpp
- 2.5k
- vllm
- 8.1k
Stars delta
- llama.cpp
- +5.7k (30d)
- vllm
- +4.2k (30d)
Open issues delta
- llama.cpp
- +522 (30d)
- vllm
- +1872 (30d)
OSV dependency advisories
- llama.cpp
- No published findings from this source as of 2026-09-18
- vllm
- No lockfile (source not queried)
Full report
- llama.cpp
- Trust report
- vllm
- Trust report
Typed relationship
llama.cpp alternative vllm`VLLM` and `llama.cpp` both offer high-throughput and efficient LLM inference engines, thus they are considered alternatives.
Choose llama.cpp if…
- llama.cpp is primarily C++; vllm is Python.
- License: llama.cpp is MIT, vllm is Apache-2.0.
- `VLLM` and `llama.cpp` both offer high-throughput and efficient LLM inference engines, thus they are considered alternatives.
- 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.
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.
Choose vllm if…
- vllm is primarily Python; llama.cpp is C++.
- License: vllm is Apache-2.0, llama.cpp is MIT.
- Requirements: Min 8 GB RAM; Installation can be done via `uv pip install vllm` or building from source for development purposes..
- `VLLM` and `llama.cpp` both offer high-throughput and efficient LLM inference engines, thus they are considered alternatives.
- Tags unique to vllm: amd, blackwell, cuda, deepseek.
- When you need a high-throughput and memory-efficient inference engine for large language models, especially if you are working with a variety of hardware configurations including AMD, CUDA, and TPU.
When NOT to use vllm
- If your project strictly requires a specific competitor's ecosystem or features that are not covered by vLLM, such as specialized model serving capabilities not supported by vLLM.
- When the project's hardware setup is incompatible with the supported hardware configurations of vLLM, such as certain non-supported GPU models or configurations.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ggml-org/llama.cpp) · observed Sep 20, 2026
- GitHub forks (ggml-org/llama.cpp) · observed Sep 20, 2026
- Last push (ggml-org/llama.cpp) · observed Sep 18, 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 (vllm-project/vllm) · observed Sep 20, 2026
- GitHub forks (vllm-project/vllm) · observed Sep 20, 2026
- Last push (vllm-project/vllm) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: llama.cpp 129k · vllm 92k (synced Sep 20, 2026).
Common questions
- What is the difference between llama.cpp and vllm?
- llama.cpp: LLM inference in C/C++. vllm: A high-throughput and memory-efficient inference and serving engine for LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose llama.cpp over vllm?
- Choose llama.cpp over vllm when llama.cpp is primarily C++; vllm is Python; License: llama.cpp is MIT, vllm is Apache-2.0;
VLLMandllama.cppboth offer high-throughput and efficient LLM inference engines, thus they are considered alternatives; 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. - When should I choose vllm over llama.cpp?
- Choose vllm over llama.cpp when vllm is primarily Python; llama.cpp is C++; License: vllm is Apache-2.0, llama.cpp is MIT; Requirements: Min 8 GB RAM; Installation can be done via
uv pip install vllmor building from source for development purposes.;VLLMandllama.cppboth offer high-throughput and efficient LLM inference engines, thus they are considered alternatives; Tags unique to vllm: amd, blackwell, cuda, deepseek; When you need a high-throughput and memory-efficient inference engine for large language models, especially if you are working with a variety of hardware configurations including AMD, CUDA, and TPU. - 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 vllm?
- If your project strictly requires a specific competitor's ecosystem or features that are not covered by vLLM, such as specialized model serving capabilities not supported by vLLM. When the project's hardware setup is incompatible with the supported hardware configurations of vLLM, such as certain non-supported GPU models or configurations.
- Is llama.cpp or vllm more popular on GitHub?
- llama.cpp has more GitHub stars (128,639 vs 92,075). Stars measure visibility, not whether either tool fits your constraints.
- Are llama.cpp and vllm open source?
- Yes - both are open-source projects on GitHub (llama.cpp: MIT, vllm: Apache-2.0).
- Where can I find alternatives to llama.cpp or vllm?
- GraphCanon lists graph-backed alternatives at llama.cpp alternatives and vllm alternatives (llama.cpp markdown twin, vllm 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 vllm?
- llama.cpp: Very active. vllm: Very 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 vllm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llama.cpp trust report; vllm trust report.