The article presents the official home for llama.cpp, an open-source local LLM inference engine, highlighting integration with the Pi coding agent via the pi-llama plugin and broad hardware optimization.
# llama.app - Official home for llama.cpp
Source: [https://llama.app/](https://llama.app/)
## Pair it with a local coding agent\.
Run`llama serve`, install the`pi\-llama`plugin and launch[Pi](https://github.com/earendil-works/pi)\. It will automatically discover your local model\. No config, no API keys\. Files stay on your machine, requests never leave it\.
```
# 1. Serve a model
llama serve
# 2. Install the pi-llama plugin
pi install git:github.com/huggingface/pi-llama
# 3. Run Pi, everything is set
pi
```
## Optimized for any hardware\.
From your laptop to a cluster, llama\.cpp runs on whatever you have\. Same binary, same models, same hand\-tuned kernels for every GPU and CPU\.
Apple Silicon
M Ultra
RTX 5090
CPU
Jetson
H100
MI300
RTX 4090
A100
M Pro
M Max
DGX Spark
T4
Radeon RX
B200
Intel Arc
RTX 3090
The article draws a parallel between llama.cpp and Linux, positioning the open-source library as foundational infrastructure for running large language models.
llama.cpp is an open-source C/C++ library for efficient LLM inference on local hardware, supporting various quantization methods and multiple backends (CPU, GPU, etc.).
Llama.cpp announces a new website and unified 'llama' binary for simpler LLM inference, along with updates like Hugging Face cache migration and multimodal support.