# elixir-nx/nx

Multi-dimensional arrays (tensors) and numerical definitions for Elixir

Repository: https://github.com/elixir-nx/nx
Canonical: https://ross.abutalabs.com/products/elixir-nx-nx
Language: Elixir
License Family: other
Topics: gpu, numerical, elixir, tensor, xla, pytorch, jit
Last push: 2026-08-18T16:13:06+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 97, longevity 100
- inputs: {"age_days": 2135, "days_push": 15, "days_rel": 23, "gap_med": 24, "n_releases_24m": 4}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2901, forks 230 (observed 2026-08-28T04:07:28.895044+00:00)

## What it is
Nx is an Elixir library providing multi-dimensional arrays (tensors) and numerical definitions with pluggable backends. The monorepo also includes EXLA (Google's XLA compiler backend) and Torchx (LibTorch backend) for accelerated computation.

## Use cases
- perform tensor computations in Elixir
- compile numerical code with XLA for GPU acceleration
- run numerical definitions with JIT compilation
- use PyTorch/LibTorch as a backend from Elixir
- build machine learning models in Elixir
- accelerate linear algebra workloads on GPU

## When to choose
- you want NumPy-like tensors in Elixir
- you need GPU-accelerated numerical computing in the BEAM ecosystem
- you want JIT-compiled numerical definitions via XLA or LibTorch

## When to avoid
- you need a mature deep learning ecosystem like PyTorch or TensorFlow
- your project is not on the Erlang/Elixir platform
- you need extensive pretrained model support out of the box

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, math, gpu-computing, compiler
- domain: machine-learning, deep-learning, gpu-computing, data-science
- platform: elixir
- tags: tensors, numerical-computing, xla, libtorch, jit, numerical-definitions, gpu

## Member repositories
- elixir-nx/nx (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:28.895044+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 2026-08-30T07:35:09.391750+00:00, confidence not recorded.
  - readme: https://github.com/elixir-nx/nx (fetched 2026-08-28T04:07:28.895044+00:00, sha f897586f7813)
- Data as of 2026-08-30T08:39:29.467469+00:00.