llama.cpp
A few options to get llama.cpp installed on your machine: Visit https://llama.app and follow the instructions Run with Docker - see our Docker documentation Download pre-built binaries from the releases page Build from source by cloning this repository - check out our build guide
The main goal of llama.cpp is to enable LLM (and VLM) 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 RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V 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 GPUs via MUSA) Vulkan and SYCL backend support CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
View llama.cpp on GitHub
Curated by Navid Moazzez·Updated Oct 1, 2026·1 min read·Navid.me is reader-supported. When you buy through links on this site, I may earn an affiliate commission. Learn more.
A few options to get llama.cpp installed on your machine: Visit https://llama.app and follow the instructions Run with Docker - see our Docker documentation Download pre-built binaries from the releases page Build from source by cloning this repository - check out our build guide
The main goal of llama.cpp is to enable LLM (and VLM) 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 RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V 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 GPUs via MUSA) Vulkan and SYCL backend support CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
llama.cpp at a glance
| Stars | 130k |
|---|---|
| Forks | 24k |
| Language | C++ |
| License | MIT |
| Last update | 2026-10-01 |
| Contributors | 2065 |
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Navid Moazzez
AI business strategist & AI OS builder
Navid Moazzez helps creators and founders master AI and build their own AI Operating System (AI OS) to automate their business and life.
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