gomlx/gomlx

Health v2 · maintenance only

Adoption not part of the score

1621 stars · 81 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

machine-learning deep-learning llm-training gpu-computing math machine-learning deep-learning large-language-models gpu-computing go windows wasm xla neural-networks autodiff pytorch-alternative jax tensor-computation huggingface linux macos gpu

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markdown · JSON · MCP: product_card(name="gomlx/gomlx")