# ggml-org/ggml

Tensor library for machine learning

Repository: https://github.com/ggml-org/ggml
Canonical: https://ross.abutalabs.com/products/ggml
Homepage: https://github.com/ggml-org
Language: C++
License: MIT
License Family: permissive
Topics: machine-learning
Last push: 2026-08-25T08:45:28+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 1445, "days_push": 8, "days_rel": 8, "gap_med": 5, "n_releases_24m": 34}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 15236, forks 1792 (observed 2026-08-28T04:11:09.425009+00:00)

## What it is
ggml is a plain C/C++ tensor library for machine learning with no dependencies, designed to be simple, portable, and efficient. It supports SIMD-optimized kernels across many CPU architectures plus CPU, GPU, NPU, and browser backends, with integer quantization and zero runtime memory allocations.

## Use cases
- run LLM inference on CPU at the edge
- embed a tensor engine in a C/C++ app with no dependencies
- quantize models to 2-8 bit integers for low-memory inference
- run ML inference in the browser via WebAssembly
- build custom ML inference engines like llama.cpp or whisper.cpp

## When to choose
- you need dependency-free, portable ML inference in C/C++
- you target edge devices, mobile, or WebAssembly
- you want fine-grained control over memory and quantization

## When to avoid
- you need automatic differentiation for training large models
- you prefer high-level frameworks like PyTorch or TensorFlow
- you need a broad ecosystem of pretrained model loaders and tools

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, llm-inference, gpu-computing, wasm
- domain: machine-learning, deep-learning, large-language-models, gpu-computing, embedded-systems
- platform: cross-platform, cpp, c, wasm, embedded
- tags: tensor-library, quantization, inference, no-dependencies, simd, gguf, gpu

## Member repositories
- ggml-org/ggml (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:09.425009+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:06:32.718801+00:00, confidence not recorded.
  - readme: https://github.com/ggml-org/ggml (fetched 2026-08-28T04:11:09.425009+00:00, sha 359c498c79a3)
  - homepage: https://github.com/ggml-org (fetched 2026-08-29T08:04:20.706376+00:00, sha ee836a233822)
- Data as of 2026-08-30T08:39:29.467469+00:00.