---
title: "ggml"
type: "tool"
slug: "ggml-org-ggml"
canonical_url: "https://www.graphcanon.com/tools/ggml-org-ggml"
github_url: "https://github.com/ggml-org/ggml"
homepage_url: null
stars: 14953
forks: 1710
primary_language: "C++"
license: "MIT"
categories: ["llm-frameworks", "developer-tools"]
tags: ["machine-learning", "large-language-models", "automatic-differentiation", "tensor-algebra"]
updated_at: "2026-07-07T18:30:02.31386+00:00"
---

# ggml

> Tensor library for machine learning

ggml is a low-level, cross-platform tensor library designed for efficient machine learning tasks. It supports automatic differentiation and various optimizers with no third-party dependencies.

## Facts

- Repository: https://github.com/ggml-org/ggml
- Stars: 14,953 · Forks: 1,710 · Open issues: 340 · Watchers: 148
- Primary language: C++
- License: MIT
- Last pushed: 2026-06-26T11:38:16+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [Developer Tools](/categories/developer-tools.md)

## Tags

machine-learning, large language models, automatic-differentiation, tensor-algebra

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## README (excerpt)

```text
# ggml

[Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205)

Tensor library for machine learning

***Note that this project is under active development. \
Some of the development is currently happening in the [llama.cpp](https://github.com/ggerganov/llama.cpp) and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) repos***

## Features

- Low-level cross-platform implementation
- Integer quantization support
- Broad hardware support
- Automatic differentiation
- ADAM and L-BFGS optimizers
- No third-party dependencies
- Zero memory allocations during runtime

## Build

```bash
git clone https://github.com/ggml-org/ggml
cd ggml

# install python dependencies in a virtual environment
python3.10 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# build the examples
mkdir build && cd build
cmake ..
cmake --build . --config Release -j 8
```

## GPT inference (example)

```bash
# run the GPT-2 small 117M model
../examples/gpt-2/download-ggml-model.sh 117M
./bin/gpt-2-backend -m models/gpt-2-117M/ggml-model.bin -p "This is an example"
```

For more information, checkout the corresponding programs in the [examples](examples) folder.

## Resources

- [Introduction to ggml](https://huggingface.co/blog/introduction-to-ggml)
- [The GGUF file format](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/ggml-org-ggml`](/api/graphcanon/tools/ggml-org-ggml)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_