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llama.cpp vs mlc-llm

llama.cpp (LLM inference in C/C++) vs mlc-llm (Universal LLM Deployment Engine with ML Compilation) - live GitHub stats and typed graph relationships, not marketing.

Markdown twin · llama.cpp alternatives · mlc-llm alternatives

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llama.cpp

ggml-org/llama.cpp

★ 120kpushed Jul 8, 2026

mlc-llm

mlc-ai/mlc-llm

★ 23kpushed Jul 7, 2026

Tagline

llama.cpp: LLM inference in C/C++
mlc-llm: Universal LLM Deployment Engine with ML Compilation

Stars

llama.cpp: 120k
mlc-llm: 23k

Forks

llama.cpp: 20k
mlc-llm: 2.1k

Open issues

llama.cpp: 1.8k
mlc-llm: 318

Language

llama.cpp: C++
mlc-llm: Python

Adopt for

llama.cpp: A C/C++ library for performing large language model (LLM) inference with minimal setup, enabling state-of-the-art performance across various hardware architectures.
mlc-llm: The MLC LLM tool is a machine learning compiler and high-performance deployment engine designed specifically for deploying large language models on various platforms including GPUs and web browsers. It uses MLCEngine as麾

Persona

llama.cpp: -
mlc-llm: -

Runtime

llama.cpp: -
mlc-llm: -

License

llama.cpp: MIT
mlc-llm: This tool is available under the Apache-2.0 license.

Last pushed

llama.cpp: Jul 8, 2026
mlc-llm: Jul 7, 2026

	llama.cpp	mlc-llm
Tagline	LLM inference in C/C++	Universal LLM Deployment Engine with ML Compilation
Stars	120k	23k
Forks	20k	2.1k
Open issues	1.8k	318
Language	C++	Python
Adopt for	A C/C++ library for performing large language model (LLM) inference with minimal setup, enabling state-of-the-art performance across various hardware architectures.	The MLC LLM tool is a machine learning compiler and high-performance deployment engine designed specifically for deploying large language models on various platforms including GPUs and web browsers. It uses MLCEngine as麾
Persona	-	-
Runtime	-	-
License	MIT	This tool is available under the Apache-2.0 license.
Last pushed	Jul 8, 2026	Jul 7, 2026
Categories	Inference & Serving	Inference & Serving
Trust and health
Days since push	0d	1d
Open issues (now)	1.8k	318
Security scan	No criticals	No lockfile
Full report	Trust report	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.
Requirements: - No external dependencies required for C/C++ implementation.; - Custom CUDA kernels support running LLM on NVIDIA GPUs..
Both MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations.
Tags unique to llama.cpp: rest api, hugging-face, c++, llm-inference.
When you require a lightweight and dependency-free solution for LLM inference that supports multiple hardware architectures including x86, ARM, and RISC-V.

When NOT to use llama.cpp

If you are working in an ecosystem requiring heavy use of high-level languages such as Python or Java, given `llama.cpp`'s focus on C/C++ and low-level optimizations.
When developing applications that need frequent API changes, as the updates in `libllama` and `llama-server` REST API might not align with your application’s release cycle.

GitHub Details

Choose mlc-llm if…

mlc-llm is primarily Python; llama.cpp is C++.
License: mlc-llm is Apache-2.0, llama.cpp is MIT.
Both MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations.
Tags unique to mlc-llm: llm, tvm, machine-learning-compilation, language-model.
- When you need to develop, optimize, and deploy AI models across multiple hardware platforms such as AMD GPU, NVIDIA GPU, Apple GPU, and Intel GPU.

When NOT to use mlc-llm

- If your deployment targets are primarily server-side without the need for cross-platform support, as MLC LLM focuses heavily on enabling native AI execution across various hardware.
- When only a subset of hardware is targeted and that particular hardware's ecosystem offers more specialized tools for model deployment.

GitHub Details

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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; Requirements: - No external dependencies required for C/C++ implementation.; - Custom CUDA kernels support running LLM on NVIDIA GPUs.; Both MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations; Tags unique to llama.cpp: rest api, hugging-face, c++, llm-inference; When you require a lightweight and dependency-free solution for LLM inference that supports multiple hardware architectures including x86, ARM, and RISC-V.
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; Both MLC-LLM and llama.cpp are focused on LLM inference but with different hardware support and optimizations; Tags unique to mlc-llm: llm, tvm, machine-learning-compilation, language-model; - When you need to develop, optimize, and deploy AI models across multiple hardware platforms such as AMD GPU, NVIDIA GPU, Apple GPU, and Intel GPU.
When should I avoid llama.cpp?: If you are working in an ecosystem requiring heavy use of high-level languages such as Python or Java, given `llama.cpp`'s focus on C/C++ and low-level optimizations. When developing applications that need frequent API changes, as the updates in `libllama` and `llama-server` REST API might not align with your application’s release cycle.
When should I avoid mlc-llm?: - If your deployment targets are primarily server-side without the need for cross-platform support, as MLC LLM focuses heavily on enabling native AI execution across various hardware. - When only a subset of hardware is targeted and that particular hardware's ecosystem offers more specialized tools for model deployment.
Is llama.cpp or mlc-llm more popular on GitHub?: llama.cpp has more GitHub stars (119,640 vs 22,917). 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 /tools/ggml-org-llama-cpp/alternatives and /tools/mlc-ai-mlc-llm/alternatives (/tools/ggml-org-llama-cpp/alternatives.md, /tools/mlc-ai-mlc-llm/alternatives.md), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?: Yes. The markdown twin at /compare/ggml-org-llama-cpp-vs-mlc-ai-mlc-llm.md 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: 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 mlc-llm?: GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llama.cpp: /tools/ggml-org-llama-cpp/trust; mlc-llm: /tools/mlc-ai-mlc-llm/trust.