# gomlx/gomlx

GoMLX: An Accelerated Machine Learning Framework For Go

Repository: https://github.com/gomlx/gomlx
Canonical: https://ross.abutalabs.com/products/gomlx
Language: Go
License: Apache-2.0
License Family: permissive
Topics: go, golang, machine-learning, neural-network, xla
Last push: 2026-08-25T09:32:05+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 94, longevity 87
- inputs: {"age_days": 1228, "days_push": 8, "days_rel": 43, "gap_med": 9, "n_releases_24m": 36}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1621, forks 81 (observed 2026-08-28T04:05:12.506492+00:00)

## What it is
GoMLX is an accelerated machine learning and math framework for Go, comparable to PyTorch/JAX/TensorFlow. It offers differentiable operators, model training and fine-tuning (including HuggingFace models), and backends ranging from a portable pure-Go implementation (also running in WASM) to an OpenXLA-based JIT backend for CPUs, GPUs, and TPUs with distributed execution.

## Use cases
- train neural networks in go
- fine-tune llms in golang
- run machine learning models without python
- accelerate tensor math on gpu with go
- load huggingface models in go
- run ml inference in the browser via wasm
- distributed training on multiple gpus or tpus

## When to choose
- you want to build or train ML models natively in Go without a Python dependency
- you need GPU/TPU acceleration via XLA from Go code
- you want portable inference including WASM in the browser

## When to avoid
- you need the breadth of pretrained models and ecosystem tooling of PyTorch
- you require production-hardened distributed training at very large scale
- your team works primarily in Python

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-training, gpu-computing, math
- domain: machine-learning, deep-learning, large-language-models, gpu-computing
- platform: go, windows, wasm
- tags: xla, neural-networks, autodiff, pytorch-alternative, jax, tensor-computation, huggingface, linux, macos, gpu

## Member repositories
- gomlx/gomlx (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:12.506492+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 2026-08-30T03:49:14.138476+00:00, confidence not recorded.
  - readme: https://github.com/gomlx/gomlx (fetched 2026-08-28T04:05:12.506492+00:00, sha 24d44bf5e849)
- Data as of 2026-08-30T08:39:29.467469+00:00.