GraphCanon updated 2w · GitHub synced 2w · 25 views this month
Decision brief
llama.cpp is a C++ framework for LLM inference, offering versatile installation options including package managers, Docker, and binary downloads.
Good fit when
- - You need high-performance inference capabilities in a lightweight environment where C++ performance benefits are critical.
- - Your deployment requires direct model quantization which can be optimized with the provided tools and framework.
Avoid when
- - 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.
- Hosting:
- unknown - 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.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No criticals
- As of 1mo
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Backing
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- Company
- ggml·GitHub org profile·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
git clone https://github.com/ggml-org/llama.cppHow it fits your stack(18)
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Evidence and technical details
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Overview
llama.cpp provides a framework for LLM inference using C++. It supports installation via package managers, Docker, pre-built binaries, and source builds.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 7, 2026
- Languages
- c++, python
Source: github.language+pyproject.toml · Aug 7, 2026
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README
Quick start
A few options to get llama.cpp installed on your machine:
- Visit https://llama.app and follow the instructions
- Run with Docker - see our Docker documentation
- Download pre-built binaries from the releases page
- Build from source by cloning this repository - check out our build guide
Once installed:
For agents
This page has a .md twin and JSON over the API.