Llama.cpp
Llama.cpp is an open-source C/C++ library for running large language models (LLMs) locally on consumer hardware.
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Llama.cpp is an open-source C/C++ library that enables running large language models efficiently on consumer-grade hardware, including CPUs and GPUs, using the GGUF model format.
Llama.cpp is an open-source C/C++ library for running large language models (LLMs) locally on consumer hardware. Created by Georgi Gerganov in March 2023, the project was initially a proof-of-concept to run Meta's LLaMA model on a MacBook. It quickly grew into one of the most popular open-source AI projects on GitHub, with 115,000 stars and over 19,000 forks as of June 2026.
The library uses the GGML tensor library and the GGUF model format, enabling efficient inference on CPUs, GPUs (CUDA, Vulkan, Metal), and other accelerators. Llama.cpp is not a company; it is a community-maintained project under the ggml-org organization on GitHub. It has no formal headquarters, employees, revenue, or funding rounds.
The project's development is driven by hundreds of contributors, with over 9,500 commits and 6,400 tags as of June 2026. The most recent stable release is b4991 as of January 2026. Llama.cpp has become the de facto standard for running LLMs locally, used by millions of developers, researchers, and hobbyists.
It supports a wide range of models, including LLaMA, Mistral, Falcon, Gemma, and many others, through the GGUF format. The project has spawned a large ecosystem of tools and applications, including Ollama, LM Studio, and LocalAI, which build on top of llama.cpp to provide user-friendly interfaces. In March 2026, a critical vulnerability (CVE-2026-33298) was disclosed in llama.cpp, an integer overflow in the ggml_nbytes function that could allow remote code execution via crafted GGUF files.
The vulnerability was patched in version b7824. This incident highlighted the security risks of the open-source supply chain for AI models. Despite this, llama.cpp remains the most widely used local inference engine, with no direct commercial entity behind it.
The project's sustainability relies on community contributions and donations via GitHub Sponsors. As of June 2026, there is no indication of any corporate entity or venture funding for the project itself.
Who buys this
- Individual developers and hobbyists running LLMs on personal computers
- Researchers needing local inference for experimentation without cloud costs
- Enterprise teams deploying on-premise or air-gapped AI solutions
- Open-source AI tool developers (e.g., Ollama, LM Studio) integrating llama.cpp as a backend
- Privacy-conscious users requiring local processing of sensitive data
Strengths and what to watch
Strengths
- Massive community adoption with 115,000 GitHub stars and 19,000 forks, making it the most popular open-source LLM inference engine
- Broad hardware support including CPUs, CUDA, Vulkan, Metal, and OpenCL, enabling deployment on diverse consumer and enterprise hardware
- Standardized GGUF model format that has become the de facto format for local LLM distribution, supported by hundreds of models
Watch for
- Critical security vulnerability CVE-2026-33298 disclosed in March 2026, an integer overflow allowing RCE via malicious GGUF files, which could erode trust in the project's security
- No formal corporate entity, revenue, or funding model, making long-term maintenance and security patching dependent on volunteer contributors
- Potential fragmentation as commercial forks or alternatives (e.g., MLX, TensorRT-LLM) offer better performance on specific hardware or better security guarantees
Recent moves
Key Information
- Industry
- Local AI
- Founded
- 2023
Frequently Asked Questions
What is Llama.cpp and what does it do?
Llama.cpp is an open-source C/C++ library for running large language models efficiently on consumer hardware like CPUs and GPUs. It uses the GGUF model format and supports CUDA, Vulkan, and Metal, enabling local inference without cloud services.
How does Llama.cpp let you run LLMs on a regular computer?
Llama.cpp optimizes inference using the GGML tensor library and the GGUF format, allowing models to run on CPUs and GPUs with minimal memory. It supports consumer hardware like MacBooks, making local LLM deployment practical for developers and hobbyists.
What is the GGUF model format used by Llama.cpp?
GGUF is a standardized file format for storing large language models, designed for efficient loading and inference. It has become the de facto format for local LLM distribution, supported by hundreds of models and tools like Ollama and LM Studio.
Is Llama.cpp safe to use after the CVE-2026-33298 vulnerability?
A critical integer overflow vulnerability (CVE-2026-33298) was patched in version b7824. Users should update to the latest version to prevent remote code execution from malicious GGUF files. The project remains widely used but relies on community-driven security fixes.
Who maintains Llama.cpp and is it backed by a company?
Llama.cpp is a community-maintained project under the ggml-org organization on GitHub, with no corporate entity, revenue, or funding. Development is driven by hundreds of volunteers, supported by donations via GitHub Sponsors.
What tools and applications are built on top of Llama.cpp?
Popular tools like Ollama, LM Studio, and LocalAI build on Llama.cpp to provide user-friendly interfaces for running LLMs locally. These tools leverage Llama.cpp's efficient inference engine, making local AI accessible to a broader audience.
Sources
- github.com — GitHub repository with 115k stars, 19.2k forks, 9,536 commits, 6,480 tags, and project structure
- www.sentinelone.com — Disclosure of CVE-2026-33298, an integer overflow vulnerability in ggml_nbytes function affecting versions prior to b7824
- buttondown.com — Weekly GitHub report showing current version b4991 as of January 2026, active issues, and contributor activity
- techcrunch.com — TechCrunch article listing AI companies raising $100M+ in 2026; llama.cpp is not listed, confirming no venture funding