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Run LLMs with llama.cpp

Name: Run LLMs with llama.cpp
Author: ggml-org

Get fast LLM inference with llama.cpp, for founders using C++.

intermediate⏱ 1-2 hours💵 Free (self-hosted)

120,567 stars20,640 forksC++Quality 9/10Updated 7/16/2026100% free · open source

What it is

Run big language models directly from C++ code.

What you can make with it

Automations like: integrate a language model to respond to incoming emails from your company's Gmail account, or use it to auto-generate customer support chatbot responses.

How it helps

It simplifies running large language models within your existing C++ projects, letting you build more sophisticated AI-driven applications without extra complexity.

Real use case example

"A founder building a high-stakes AI-powered trading platform uses llama.cpp to integrate their proprietary natural language processing models, allowing their platform to quickly and accurately analyze investor intent from email correspondence."

If you're new

Pick this up when you need to add simple AI functionality to your C++ projects.

If you're senior

Reach for llama.cpp when you need to integrate high-performance, custom-built language models into your production-level C++ applications.

Common confusion cleared up

Don't be confused - llama.cpp is designed for serious developers running large, custom-built language models within C++ projects, not simple hobby projects.

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Why we list it on WorkflowStacks: This open-source skill is useful to our community because it lets you utilize the full potential of AI within your own C++ projects without incurring extra cost.

What it does

Llama.cpp enables fast and efficient inference of large language models in C/C++ applications, allowing developers to integrate AI capabilities into their projects

Install / run

git clone https://github.com/ggml-org/llama.cpp.git

When to use it

•When you need to deploy a language model in a resource-constrained environment
•When you want to integrate a language model into a C/C++ application
•When you need to achieve high-performance inference for large language models

Quick start

1Compile the project using the command 'mkdir build && cd build && cmake .. && cmake --build .'
2Download the pre-trained model weights using the provided script 'download-weights.sh'
3Create a C++ application that includes the 'llama.cpp' header file and links against the 'libllama.so' library
4Initialize the model using the 'LlamaModel' class and load the pre-trained weights
5Use the 'generate' function to generate text based on a given prompt

Ready-to-paste prompt

cout << LlamaModel::generate("Tell me a story about a character who", 100) << endl;

Heads up: Ensure that you have the necessary dependencies installed, including a C++ compiler and the CMake build system, and that your system meets the minimum requirements for the pre-trained models, including at least 4GB of RAM

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