What is LLAMA CPP?

LLAMA CPP, also written as Llama.cpp, is an open-source C++ library designed for efficient inference of large language models (LLMs). Here's a breakdown of its key aspects:

Purpose:

LLAMA CPP provides a high-performance way to run inference tasks with pre-trained LLM models.

Inference refers to the process of using a trained LLM model to generate text, translate languages, write different kinds of creative content, or answer your questions in an informative way.

Functionality:

LLAMA CPP is written in C++, making it fast and versatile. It can integrate seamlessly with various programming languages through bindings.

It supports a wide range of LLM models packaged in the GGUF file format, which is efficient for CPU-only and mixed CPU/GPU environments.

Benefits:

Efficiency: LLAMA CPP is known for its speed and optimized memory usage, making it suitable for real-time LLM applications.

Cross-Platform Compatibility: Due to its C++ core, LLAMA CPP is compatible with a broad range of operating systems and hardware architectures.

Open-Source: Being open-source allows for community contributions and transparent development.

Additional Points:

LLAMA CPP offers an OpenAI API-compatible HTTP server, enabling you to connect existing LLM clients to locally hosted models.

It has a vibrant developer community with extensive documentation and various bindings for languages like Python, Go, Node.js, and Rust. This facilitates integration with different development environments.

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware - locally and in the cloud.

Plain C/C++ implementation without any dependencies

Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks

AVX, AVX2 and AVX512 support for x86 architectures

1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use

Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)

Vulkan, SYCL, and (partial) OpenCL backend support

CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity