llama.cpp makes local LLM deployment possible on constrained hardware, including Raspberry Pi systems. It is especially useful when memory, compute, cost, or network access makes a hosted endpoint impractical, though edge inference may remain slow. section 8.4 section 11.5
A practical deployment typically uses llama.cpp to:
- Compile the C++ project and its dependencies for the target architecture. section 11.1.3
- Quantize models when memory limits require smaller inference footprints. section 11.4.1 section 11.5
- Serve a model locally and connect it to a custom frontend or application. section 11.4.1
- Support multimodal models on suitably configured edge devices. section 11.5
Building on a Raspberry Pi
A repeatable setup updates the system, installs Git and pip, clones llama.cpp, checks out a known commit, creates a Python virtual environment, installs requirements, and builds the project. CMake may be preferable when the environment differs from the tested setup. section 11.1.3
$ sudo apt update && sudo apt upgrade -y
Begin by updating the operating system before installing the project dependencies. section 11.1.3
$ git clone https://github.com/ggerganov/llama.cpp.git $ cd llama.cpp
Clone the repository locally, then use a fixed commit when reproducibility matters because the project is described as lacking versioning. section 11.1.3
$ make
Compile the C++ project for the device after preparing its environment. section 11.1.3
