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ggml-org/llama.cpp

🔥 Hot★ 108,653C++Audience · developerComplexity · 3/5ActiveLicenseSetup · moderate

TLDR

Run large language models locally on your computer or server using optimized C++ code, with no heavy dependencies or external APIs required.

Mindmap

mindmap
  root((llama.cpp))
    What it does
      Local LLM inference
      Offline text generation
      OpenAI-compatible API
    Hardware support
      Apple Silicon optimized
      NVIDIA CUDA GPUs
      AMD and Intel CPUs
    Key features
      Model quantization
      Hybrid CPU-GPU inference
      Multimodal support
    Use cases
      Private chat servers
      Offline applications
      Edge deployment

Things people build with this

USE CASE 1

Run open-source AI chatbots on your own hardware without relying on cloud APIs.

USE CASE 2

Deploy a private language model server for a team or organization with sensitive data.

USE CASE 3

Build applications that work offline or on edge devices with limited resources.

USE CASE 4

Experiment with different language models locally before deciding which to use in production.

Tech stack

CC++CUDAMetalVulkanARM NEONHIP

Getting it running

Difficulty · moderate Time to first run · 30min

Requires downloading a model file (potentially gigabytes) and choosing appropriate build flags for your hardware (CPU vs CUDA vs Metal).

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

In plain English

llama.cpp is a project for running large language models, the kind of AI that powers chatbots, on your own computer or server, written in plain C and C++. The problem it solves is that most language model code is written in Python and depends on heavy machine-learning frameworks; llama.cpp instead aims for LLM inference (using a trained model to generate text) with minimal setup and high performance on a wide range of hardware, locally and in the cloud, with no external dependencies. The way it works is by loading model weight files in the GGUF format and running them with hand-tuned code that uses each platform's fastest path. Apple Silicon is treated as a first-class citizen, with optimizations through ARM NEON, the Accelerate framework, and Metal. There is also support for various x86 instruction sets (AVX, AVX2, AVX512, AMX), RISC-V vector extensions, custom CUDA kernels for NVIDIA GPUs, HIP for AMD, MUSA for Moore Threads, and Vulkan and SYCL backends. It supports integer quantization from 1.5-bit up to 8-bit, which shrinks models so they fit on smaller machines, and CPU-plus-GPU hybrid inference for models too big for VRAM alone. The repository includes command-line tools (llama-cli) and a server (llama-server) that exposes an OpenAI-compatible REST API, plus multimodal and Hugging Face cache integration. You would use llama.cpp when you want to run open-weight LLMs offline, host a private chat server, build apps that don't depend on external API providers, or deploy on hardware where heavy frameworks are too slow. Many model families are supported. The project is MIT-licensed. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
How do I download and run an open-source language model using llama.cpp on my Mac?
Prompt 2
Show me how to set up llama.cpp as a local API server that's compatible with OpenAI's API format.
Prompt 3
What's the difference between running a model with and without quantization in llama.cpp, and how do I enable it?
Prompt 4
How can I use llama.cpp to run a language model on both my CPU and GPU at the same time?
Prompt 5
What model files work with llama.cpp and where can I find them?
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Generated 2026-05-21 · Model: sonnet-4-6 · Verify against the repo before relying on details.