llama.cpp
ggml-org/llama.cpp
C/C++ inference engine to run Llama and other LLMs efficiently on CPUs and consumer hardware. GitHub: ggml-org/llama.cpp (~123k stars).
GitHub repository
Overview
llama.cpp is the open-source project at ggml-org/llama.cpp on GitHub (https://github.com/ggml-org/llama.cpp). C/C++ inference engine to run Llama and other LLMs efficiently on CPUs and consumer hardware. Category on LimeDock: ai-powered. Approximate community size: ~123k GitHub stars. Repo (copy/paste): https://github.com/ggml-org/llama.cpp This LimeDock Directories page explains what it is and how teams use it in plain English — so searches for the GitHub project can land on a practical guide.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/ggml-org/llama.cpp GitHub path: ggml-org/llama.cpp
How to open it: 1. Copy this URL: https://github.com/ggml-org/llama.cpp 2. Clone or follow the README install for your OS / stack. 3. Start with the smallest example in their docs before production use.
How to use it
**Simple example** You need local inference without Python ML stacks.
1. Build llama.cpp for your OS. 2. Download a GGUF model you can use. 3. Run a local CLI chat, then integrate the server API if needed.
Example prompts
- “Build llama.cpp and run a small GGUF model.”
- “When to choose llama.cpp vs Ollama?”
- “Tune threads/context for a 16GB machine.”
Use cases and examples
- Evaluate ggml-org/llama.cpp for your stack
- Onboard a teammate to llama.cpp with a shared checklist
- Compare llama.cpp against tools you already pay for
- Capture lessons in your internal wiki after a pilot
Prerequisites
- Build tools (cmake/make)
- Disk for GGUF models
Tips
- Ollama is friendlier; llama.cpp is more controllable.
- Watch model licenses.