Package: llama.cpp-examples (5882+dfsg-3)

[ Source: llama.cpp  ]

LLM inference in C/C++ - example programs

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range 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, AVX512 and AMX 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 and Moore Threads MTT GPUs via MUSA)
 * Vulkan and SYCL backend support
 * CPU+GPU hybrid inference to partially accelerate models larger than the
   total VRAM capacity

The compute functionality is provided by ggml. By default, ggml's CPU backend is installed, but there are many other backends for CPUs and GPUs.

This package contains utilities that usptream ships as examples.

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• dep: libgcc-s1 (>= 3.0) [amd64, arm64]

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• dep: libggml0 (>= 0.0~git20250712.d62df60)

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Architecture	Package Size	Installed Size	Files
amd64	2,866.7 kB	27,617.0 kB	[list of files]
arm64	2,520.3 kB	26,377.0 kB	[list of files]
ppc64 (unofficial port)	2,561.4 kB	33,481.0 kB	[list of files]
ppc64el	2,526.7 kB	31,305.0 kB	[list of files]