Comparison
llama.cpp vs mlc-llm
Verdict
Pick llama.cpp if llama.cpp is a C++ framework for LLM inference, offering versatile installation options including package managers, Docker, and binary downloads; pick mlc-llm if mature deployment engine for efficient large-scale model serving, leveraging advanced compilation techniques.
Markdown twin · llama.cpp alternatives · mlc-llm alternatives
GraphCanon updated 4d
Trust & integrity
| Signal | llama.cpp | mlc-llm |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Active (16d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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++
- mlc-llm
- Universal LLM Deployment Engine with ML Compilation
Stars
- llama.cpp
- 123k
- mlc-llm
- 23k
Forks
- llama.cpp
- 21k
- mlc-llm
- 2.1k
Open issues
- llama.cpp
- 2.0k
- mlc-llm
- 334
Language
- llama.cpp
- C++
- mlc-llm
- Python
Adopt for
- llama.cpp
- llama.cpp is a C++ framework for LLM inference, offering versatile installation options including package managers, Docker, and binary downloads.
- mlc-llm
- Mature deployment engine for efficient large-scale model serving, leveraging advanced compilation techniques.
Persona
- llama.cpp
- -
- mlc-llm
- -
Runtime
- llama.cpp
- -
- mlc-llm
- -
License
- llama.cpp
- MIT licensed, allowing free use and modification under certain conditions.
- mlc-llm
- Open-source under the Apache-2.0 license, allowing for free use in both open source and commercial contexts while requiring acknowledgment of its use.
Last pushed
- llama.cpp
- Aug 7, 2026
- mlc-llm
- Jul 31, 2026
Categories
- llama.cpp
- Inference & Serving
- mlc-llm
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- llama.cpp
- Very active (96%)
- mlc-llm
- Active (82%)
Days since push
- llama.cpp
- 0d
- mlc-llm
- 16d
Open issues (now)
- llama.cpp
- 2.0k
- mlc-llm
- 334
Stars delta
- llama.cpp
- +3.4k (30d)
- mlc-llm
- +103 (30d)
Open issues delta
- llama.cpp
- +143 (30d)
- mlc-llm
- +11 (30d)
OSV dependency advisories
- llama.cpp
- No published findings from this source as of 2026-07-11
- mlc-llm
- No lockfile (source not queried)
Full report
- llama.cpp
- Trust report
- mlc-llm
- Trust report
Typed relationship
llama.cpp alternative mlc-llmBoth MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations.
Choose llama.cpp if…
- llama.cpp is primarily C++; mlc-llm is Python.
- License: llama.cpp is MIT, mlc-llm is Apache-2.0.
- llama.cpp supports various installation methods including package managers (like brew), Docker containers for isolation, pre-built binaries for ease of deployment, and source builds for flexibility.
- Requirements: Installation can be done via multiple channels including package managers, Docker, and direct downloads..
- Both MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations.
- Tags unique to llama.cpp: c++, ggml.
- - You need high-performance inference capabilities in a lightweight environment where C++ performance benefits are critical.
When NOT to use llama.cpp
- - If you prefer a language other than C++, as this tool lacks support for Python or JavaScript bindings that provide higher-level abstractions.
- - When your project demands extensive runtime customization and flexibility that is more easily achieved in languages like Python with libraries such as PyTorch.
Choose mlc-llm if…
- mlc-llm is primarily Python; llama.cpp is C++.
- License: mlc-llm is Apache-2.0, llama.cpp is MIT.
- Requirements: - Requires familiarity with Python and machine learning concepts.; - Efficient with large language models but may have higher initial setup complexity due to specialized features..
- Both MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations.
- Tags unique to mlc-llm: language-model, llm, machine-learning-compilation, tvm.
- Also covers LLM Frameworks.
- - When you need an efficient tool specifically designed with advanced compilation techniques that optimize performance for large language models (LLMs).
When NOT to use mlc-llm
- - Avoid mlc-llm if you are looking for a broader suite of tools; this tool focuses intensely on deployment efficiency via ML compilation techniques.
- - If you prefer tools with extensive third-party integrations or community-developed extensions, as mlc-llm's focus is narrow to deep optimization.
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 Aug 7, 2026
- GitHub forks (ggml-org/llama.cpp) · observed Aug 7, 2026
- Last push (ggml-org/llama.cpp) · observed Aug 7, 2026
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (mlc-ai/mlc-llm) · observed Aug 17, 2026
- GitHub forks (mlc-ai/mlc-llm) · observed Aug 17, 2026
- Last push (mlc-ai/mlc-llm) · observed Jul 31, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: llama.cpp 123k · mlc-llm 23k (synced Aug 7, 2026).
Common questions
- What is the difference between llama.cpp and mlc-llm?
- llama.cpp: LLM inference in C/C++. mlc-llm: Universal LLM Deployment Engine with ML Compilation. See the comparison table for live GitHub stats and shared categories.
- When should I choose llama.cpp over mlc-llm?
- Choose llama.cpp over mlc-llm when llama.cpp is primarily C++; mlc-llm is Python; License: llama.cpp is MIT, mlc-llm is Apache-2.0; llama.cpp supports various installation methods including package managers (like brew), Docker containers for isolation, pre-built binaries for ease of deployment, and source builds for flexibility; Requirements: Installation can be done via multiple channels including package managers, Docker, and direct downloads.; Both MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations; Tags unique to llama.cpp: c++, ggml; - You need high-performance inference capabilities in a lightweight environment where C++ performance benefits are critical.
- When should I choose mlc-llm over llama.cpp?
- Choose mlc-llm over llama.cpp when mlc-llm is primarily Python; llama.cpp is C++; License: mlc-llm is Apache-2.0, llama.cpp is MIT; Requirements: - Requires familiarity with Python and machine learning concepts.; - Efficient with large language models but may have higher initial setup complexity due to specialized features.; Both MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations; Tags unique to mlc-llm: language-model, llm, machine-learning-compilation, tvm; Also covers LLM Frameworks; - When you need an efficient tool specifically designed with advanced compilation techniques that optimize performance for large language models (LLMs).
- When should I avoid llama.cpp?
- - If you prefer a language other than C++, as this tool lacks support for Python or JavaScript bindings that provide higher-level abstractions. - When your project demands extensive runtime customization and flexibility that is more easily achieved in languages like Python with libraries such as PyTorch.
- When should I avoid mlc-llm?
- - Avoid mlc-llm if you are looking for a broader suite of tools; this tool focuses intensely on deployment efficiency via ML compilation techniques. - If you prefer tools with extensive third-party integrations or community-developed extensions, as mlc-llm's focus is narrow to deep optimization.
- Is llama.cpp or mlc-llm more popular on GitHub?
- llama.cpp has more GitHub stars (122,941 vs 23,063). Stars measure visibility, not whether either tool fits your constraints.
- Are llama.cpp and mlc-llm open source?
- Yes - both are open-source projects on GitHub (llama.cpp: MIT, mlc-llm: Apache-2.0).
- Where can I find alternatives to llama.cpp or mlc-llm?
- GraphCanon lists graph-backed alternatives at llama.cpp alternatives and mlc-llm alternatives (llama.cpp markdown twin, mlc-llm 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 mlc-llm?
- llama.cpp: Very active. mlc-llm: 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 mlc-llm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llama.cpp trust report; mlc-llm trust report.